In this notebook, some template code has already been provided for you, and you will need to implement additional functionality to successfully complete this project. You will not need to modify the included code beyond what is requested. Sections that begin with '(IMPLEMENTATION)' in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section, and the specifics of the implementation are marked in the code block with a 'TODO' statement. Please be sure to read the instructions carefully!
Note: Once you have completed all of the code implementations, you need to finalize your work by exporting the iPython Notebook as an HTML document. Before exporting the notebook to html, all of the code cells need to have been run so that reviewers can see the final implementation and output. You can then export the notebook by using the menu above and navigating to \n", "File -> Download as -> HTML (.html). Include the finished document along with this notebook as your submission.
In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a 'Question X' header. Carefully read each question and provide thorough answers in the following text boxes that begin with 'Answer:'. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.
Note: Code and Markdown cells can be executed using the Shift + Enter keyboard shortcut. Markdown cells can be edited by double-clicking the cell to enter edit mode.
The rubric contains optional "Stand Out Suggestions" for enhancing the project beyond the minimum requirements. If you decide to pursue the "Stand Out Suggestions", you should include the code in this IPython notebook.
In this notebook, you will make the first steps towards developing an algorithm that could be used as part of a mobile or web app. At the end of this project, your code will accept any user-supplied image as input. If a dog is detected in the image, it will provide an estimate of the dog's breed. If a human is detected, it will provide an estimate of the dog breed that is most resembling. The image below displays potential sample output of your finished project (... but we expect that each student's algorithm will behave differently!).

In this real-world setting, you will need to piece together a series of models to perform different tasks; for instance, the algorithm that detects humans in an image will be different from the CNN that infers dog breed. There are many points of possible failure, and no perfect algorithm exists. Your imperfect solution will nonetheless create a fun user experience!
We break the notebook into separate steps. Feel free to use the links below to navigate the notebook.
In the code cell below, we import a dataset of dog images. We populate a few variables through the use of the load_files function from the scikit-learn library:
train_files, valid_files, test_files - numpy arrays containing file paths to imagestrain_targets, valid_targets, test_targets - numpy arrays containing onehot-encoded classification labels dog_names - list of string-valued dog breed names for translating labelsfrom sklearn.datasets import load_files
from keras.utils import np_utils
import numpy as np
from glob import glob
# define function to load train, test, and validation datasets
def load_dataset(path):
data = load_files(path)
dog_files = np.array(data['filenames'])
dog_targets = np_utils.to_categorical(np.array(data['target']), 133)
return dog_files, dog_targets
dogImages ='C:/Training/udacity/AI_NanoDegree/Term2/3.Convolutional Neural Networks Videos/datasets/dogImages'
# load train, test, and validation datasets
#train_files, train_targets = load_dataset('dogImages/train')
#valid_files, valid_targets = load_dataset('dogImages/valid')
#test_files, test_targets = load_dataset('dogImages/test')
train_files, train_targets = load_dataset(dogImages+'/train')
valid_files, valid_targets = load_dataset(dogImages+'/valid')
test_files, test_targets = load_dataset(dogImages+'/test')
# load list of dog names
#dog_names = [item[20:-1] for item in sorted(glob("dogImages/train/*/"))]
dog_names = [item[108:-1] for item in glob(dogImages+'/train/*/')]
#print(dog_names,'\n')
# print statistics about the dataset
print('There are %d total dog categories.' % len(dog_names))
print('There are %s total dog images.\n' % len(np.hstack([train_files, valid_files, test_files])))
print('There are %d training dog images.' % len(train_files))
print('There are %d validation dog images.' % len(valid_files))
print('There are %d test dog images.'% len(test_files))
Using TensorFlow backend.
There are 133 total dog categories. There are 8351 total dog images. There are 6680 training dog images. There are 835 validation dog images. There are 836 test dog images.
In the code cell below, we import a dataset of human images, where the file paths are stored in the numpy array human_files.
import random
random.seed(8675309)
datadir ='C:/Training/udacity/AI_NanoDegree/Term2/3.Convolutional Neural Networks Videos/datasets/'
# load filenames in shuffled human dataset
#human_files = np.array(glob(datadir+"lfw/*/*"))
human_files = np.array(glob(datadir+'lfw/lfw/*/*'))
random.shuffle(human_files)
print(human_files[3])
# print statistics about the dataset
print('There are %d total human images.' % len(human_files))
C:/Training/udacity/AI_NanoDegree/Term2/3.Convolutional Neural Networks Videos/datasets/lfw/lfw\Laurence_Fishburne\Laurence_Fishburne_0001.jpg There are 13233 total human images.
def load_human_dataset(path):
data = load_files(path)
human_files1 = np.array(data['filenames'])
human_targets1 = np_utils.to_categorical(np.array(data['target']), 13233)
return human_files1, human_targets1
human_files1, human_targets1 = load_human_dataset(datadir+'lfw/lfw/')
print(human_files1[3])
print(human_targets1[3])
C:/Training/udacity/AI_NanoDegree/Term2/3.Convolutional Neural Networks Videos/datasets/lfw/lfw/Tang_Jiaxuan\Tang_Jiaxuan_0009.jpg [ 0. 0. 0. ..., 0. 0. 0.]
We use OpenCV's implementation of Haar feature-based cascade classifiers to detect human faces in images. OpenCV provides many pre-trained face detectors, stored as XML files on github. We have downloaded one of these detectors and stored it in the haarcascades directory.
In the next code cell, we demonstrate how to use this detector to find human faces in a sample image.
import cv2
import matplotlib.pyplot as plt
%matplotlib inline
# extract pre-trained face detector
#face_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_alt.xml')
face_cascade = cv2.CascadeClassifier(datadir+'opencv-master/data/'+'haarcascades/haarcascade_frontalface_alt.xml')
#print(face_cascade)
# load color (BGR) image
random.shuffle(human_files)
img = cv2.imread(human_files[3])
print("\n",human_files[3])
# convert BGR image to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# find faces in image
faces = face_cascade.detectMultiScale(gray)
# print number of faces detected in the image
print('Number of faces detected:', len(faces))
# get bounding box for each detected face
for (x,y,w,h) in faces:
# add bounding box to color image#
cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
# convert BGR image to RGB for plotting
cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# display the image, along with bounding box
plt.imshow(cv_rgb)
plt.show()
C:/Training/udacity/AI_NanoDegree/Term2/3.Convolutional Neural Networks Videos/datasets/lfw/lfw\Casey_Mears\Casey_Mears_0001.jpg Number of faces detected: 1
Before using any of the face detectors, it is standard procedure to convert the images to grayscale. The detectMultiScale function executes the classifier stored in face_cascade and takes the grayscale image as a parameter.
In the above code, faces is a numpy array of detected faces, where each row corresponds to a detected face. Each detected face is a 1D array with four entries that specifies the bounding box of the detected face. The first two entries in the array (extracted in the above code as x and y) specify the horizontal and vertical positions of the top left corner of the bounding box. The last two entries in the array (extracted here as w and h) specify the width and height of the box.
We can use this procedure to write a function that returns True if a human face is detected in an image and False otherwise. This function, aptly named face_detector, takes a string-valued file path to an image as input and appears in the code block below.
# returns "True" if face is detected in image stored at img_path
def face_detector(img_path):
img = cv2.imread(img_path)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray)
return len(faces) > 0
Question 1: Use the code cell below to test the performance of the face_detector function.
human_files have a detected human face? dog_files have a detected human face? Ideally, we would like 100% of human images with a detected face and 0% of dog images with a detected face. You will see that our algorithm falls short of this goal, but still gives acceptable performance. We extract the file paths for the first 100 images from each of the datasets and store them in the numpy arrays human_files_short and dog_files_short.
Answer:
What percentage of the first 100 images in human_files have a detected human face? = 99%
What percentage of the first 100 images in dog_files have a detected human face? = 11%
human_files_short = human_files[:100]
dog_files_short = train_files[:100]
# Do NOT modify the code above this line.
## TODO: Test the performance of the face_detector algorithm
## on the images in human_files_short and dog_files_short.
human_file_count =0
for human_file in human_files_short:
if face_detector(human_file):
human_file_count +=1
print('Human Face Detected out of %d files is %d ' % (len(human_files_short), human_file_count))
dog_file_count =0
for dog_file in dog_files_short:
if face_detector(dog_file):
dog_file_count +=1
print('Human Face Detected in %d Dog files is %d ' % (len(dog_files_short), dog_file_count))
Human Face Detected out of 100 files is 99 Human Face Detected in 100 Dog files is 11
Question 2: This algorithmic choice necessitates that we communicate to the user that we accept human images only when they provide a clear view of a face (otherwise, we risk having unneccessarily frustrated users!). In your opinion, is this a reasonable expectation to pose on the user? If not, can you think of a way to detect humans in images that does not necessitate an image with a clearly presented face?
Answer:
This is not a reasonable exceptation. Use data agumentation logic to detect humans in images that does not necessitate an image with a clearly presented face.
Example: Simple use of ImageDataGenerator for data agumentation. This sample shows rotation of image by 30 degree, width and height sift by 20% Shear and Zoom by 20% . Flipping the Image horizontally.
ImageDataGenerator(preprocessing_function=preprocess_input,
rotation_range=30,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True )
Here are the other list of parameters that you can apply to agument images:
We suggest the face detector from OpenCV as a potential way to detect human images in your algorithm, but you are free to explore other approaches, especially approaches that make use of deep learning :). Please use the code cell below to design and test your own face detection algorithm. If you decide to pursue this optional task, report performance on each of the datasets.
## (Optional) TODO: Report the performance of another
## face detection algorithm on the LFW dataset
### Feel free to use as many code cells as needed.
#print(" Before Removing Final Layer \n",model.predict(human_files).shape)
#from keras.applications.vgg16 import VGG16
#model = VGG16(include_top=False)
#model.summary()
#print(" After Removing Final Layer \n",model.predict(human_files).shape)
face_cascade1 = cv2.CascadeClassifier(datadir+'opencv-master/data/'+'haarcascades/haarcascade_frontalface_default.xml')
random.shuffle(human_files)
img1 = cv2.imread(human_files[3])
print("\n",human_files[3][109:-4])
# convert BGR image to grayscale
gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
# find faces in image
faces1 = face_cascade1.detectMultiScale(gray1,scaleFactor=1.1,
minNeighbors=5,
minSize=(20, 20)
)
# print number of faces detected in the image
print('Number of faces detected:', len(faces1))
# get bounding box for each detected face
for (x,y,w,h) in faces1:
# add bounding box to color image#
cv2.rectangle(img1,(x,y),(x+w,y+h),(255,0,0),2)
# convert BGR image to RGB for plotting
cv_rgb1 = cv2.cvtColor(img1, cv2.COLOR_BGR2RGB)
# display the image, along with bounding box
plt.imshow(cv_rgb1)
plt.show()
d\Ernie_Grunfeld_0001 Number of faces detected: 1
def face_detector1(img_path):
img1 = cv2.imread(img_path)
gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
faces1 = face_cascade1.detectMultiScale(gray1,scaleFactor=1.1,
minNeighbors=5,
minSize=(20, 20)
)
return len(faces1) > 0
human_files_short1 = human_files[:100]
dog_files_short1 = train_files[:100]
# Do NOT modify the code above this line.
## TODO: Test the performance of the face_detector algorithm
## on the images in human_files_short and dog_files_short.
human_file_count1 =0
for human_file1 in human_files_short1:
if face_detector1(human_file1):
human_file_count1 +=1
print('Human Face Detected out of %d files is %d ' % (len(human_files_short1), human_file_count1))
dog_file_count1 =0
for dog_file1 in dog_files_short1:
if face_detector1(dog_file1):
dog_file_count1 +=1
print('Human Face Detected in %d Dog files is %d ' % (len(dog_files_short1), dog_file_count1))
Human Face Detected out of 100 files is 99 Human Face Detected in 100 Dog files is 33
In this section, we use a pre-trained ResNet-50 model to detect dogs in images. Our first line of code downloads the ResNet-50 model, along with weights that have been trained on ImageNet, a very large, very popular dataset used for image classification and other vision tasks. ImageNet contains over 10 million URLs, each linking to an image containing an object from one of 1000 categories. Given an image, this pre-trained ResNet-50 model returns a prediction (derived from the available categories in ImageNet) for the object that is contained in the image.
from keras.applications.resnet50 import ResNet50
# define ResNet50 model
ResNet50_model = ResNet50(weights='imagenet')
ResNet50_model.summary()
____________________________________________________________________________________________________ Layer (type) Output Shape Param # Connected to ==================================================================================================== input_1 (InputLayer) (None, 224, 224, 3) 0 ____________________________________________________________________________________________________ zero_padding2d_1 (ZeroPadding2D) (None, 230, 230, 3) 0 ____________________________________________________________________________________________________ conv1 (Conv2D) (None, 112, 112, 64) 9472 ____________________________________________________________________________________________________ bn_conv1 (BatchNormalization) (None, 112, 112, 64) 256 ____________________________________________________________________________________________________ activation_1 (Activation) (None, 112, 112, 64) 0 ____________________________________________________________________________________________________ max_pooling2d_1 (MaxPooling2D) (None, 55, 55, 64) 0 ____________________________________________________________________________________________________ res2a_branch2a (Conv2D) (None, 55, 55, 64) 4160 ____________________________________________________________________________________________________ bn2a_branch2a (BatchNormalizatio (None, 55, 55, 64) 256 ____________________________________________________________________________________________________ activation_2 (Activation) (None, 55, 55, 64) 0 ____________________________________________________________________________________________________ res2a_branch2b (Conv2D) (None, 55, 55, 64) 36928 ____________________________________________________________________________________________________ bn2a_branch2b (BatchNormalizatio (None, 55, 55, 64) 256 ____________________________________________________________________________________________________ activation_3 (Activation) (None, 55, 55, 64) 0 ____________________________________________________________________________________________________ res2a_branch2c (Conv2D) (None, 55, 55, 256) 16640 ____________________________________________________________________________________________________ res2a_branch1 (Conv2D) (None, 55, 55, 256) 16640 ____________________________________________________________________________________________________ bn2a_branch2c (BatchNormalizatio (None, 55, 55, 256) 1024 ____________________________________________________________________________________________________ bn2a_branch1 (BatchNormalization (None, 55, 55, 256) 1024 ____________________________________________________________________________________________________ add_1 (Add) (None, 55, 55, 256) 0 ____________________________________________________________________________________________________ activation_4 (Activation) (None, 55, 55, 256) 0 ____________________________________________________________________________________________________ res2b_branch2a (Conv2D) (None, 55, 55, 64) 16448 ____________________________________________________________________________________________________ bn2b_branch2a (BatchNormalizatio (None, 55, 55, 64) 256 ____________________________________________________________________________________________________ activation_5 (Activation) (None, 55, 55, 64) 0 ____________________________________________________________________________________________________ res2b_branch2b (Conv2D) (None, 55, 55, 64) 36928 ____________________________________________________________________________________________________ bn2b_branch2b (BatchNormalizatio (None, 55, 55, 64) 256 ____________________________________________________________________________________________________ activation_6 (Activation) (None, 55, 55, 64) 0 ____________________________________________________________________________________________________ res2b_branch2c (Conv2D) (None, 55, 55, 256) 16640 ____________________________________________________________________________________________________ bn2b_branch2c (BatchNormalizatio (None, 55, 55, 256) 1024 ____________________________________________________________________________________________________ add_2 (Add) (None, 55, 55, 256) 0 ____________________________________________________________________________________________________ activation_7 (Activation) (None, 55, 55, 256) 0 ____________________________________________________________________________________________________ res2c_branch2a (Conv2D) (None, 55, 55, 64) 16448 ____________________________________________________________________________________________________ bn2c_branch2a (BatchNormalizatio (None, 55, 55, 64) 256 ____________________________________________________________________________________________________ activation_8 (Activation) (None, 55, 55, 64) 0 ____________________________________________________________________________________________________ res2c_branch2b (Conv2D) (None, 55, 55, 64) 36928 ____________________________________________________________________________________________________ bn2c_branch2b (BatchNormalizatio (None, 55, 55, 64) 256 ____________________________________________________________________________________________________ activation_9 (Activation) (None, 55, 55, 64) 0 ____________________________________________________________________________________________________ res2c_branch2c (Conv2D) (None, 55, 55, 256) 16640 ____________________________________________________________________________________________________ bn2c_branch2c (BatchNormalizatio (None, 55, 55, 256) 1024 ____________________________________________________________________________________________________ add_3 (Add) (None, 55, 55, 256) 0 ____________________________________________________________________________________________________ activation_10 (Activation) (None, 55, 55, 256) 0 ____________________________________________________________________________________________________ res3a_branch2a (Conv2D) (None, 28, 28, 128) 32896 ____________________________________________________________________________________________________ bn3a_branch2a (BatchNormalizatio (None, 28, 28, 128) 512 ____________________________________________________________________________________________________ activation_11 (Activation) (None, 28, 28, 128) 0 ____________________________________________________________________________________________________ res3a_branch2b (Conv2D) (None, 28, 28, 128) 147584 ____________________________________________________________________________________________________ bn3a_branch2b (BatchNormalizatio (None, 28, 28, 128) 512 ____________________________________________________________________________________________________ activation_12 (Activation) (None, 28, 28, 128) 0 ____________________________________________________________________________________________________ res3a_branch2c (Conv2D) (None, 28, 28, 512) 66048 ____________________________________________________________________________________________________ res3a_branch1 (Conv2D) (None, 28, 28, 512) 131584 ____________________________________________________________________________________________________ bn3a_branch2c (BatchNormalizatio (None, 28, 28, 512) 2048 ____________________________________________________________________________________________________ bn3a_branch1 (BatchNormalization (None, 28, 28, 512) 2048 ____________________________________________________________________________________________________ add_4 (Add) (None, 28, 28, 512) 0 ____________________________________________________________________________________________________ activation_13 (Activation) (None, 28, 28, 512) 0 ____________________________________________________________________________________________________ res3b_branch2a (Conv2D) (None, 28, 28, 128) 65664 ____________________________________________________________________________________________________ bn3b_branch2a (BatchNormalizatio (None, 28, 28, 128) 512 ____________________________________________________________________________________________________ activation_14 (Activation) (None, 28, 28, 128) 0 ____________________________________________________________________________________________________ res3b_branch2b (Conv2D) (None, 28, 28, 128) 147584 ____________________________________________________________________________________________________ bn3b_branch2b (BatchNormalizatio (None, 28, 28, 128) 512 ____________________________________________________________________________________________________ activation_15 (Activation) (None, 28, 28, 128) 0 ____________________________________________________________________________________________________ res3b_branch2c (Conv2D) (None, 28, 28, 512) 66048 ____________________________________________________________________________________________________ bn3b_branch2c (BatchNormalizatio (None, 28, 28, 512) 2048 ____________________________________________________________________________________________________ add_5 (Add) (None, 28, 28, 512) 0 ____________________________________________________________________________________________________ activation_16 (Activation) (None, 28, 28, 512) 0 ____________________________________________________________________________________________________ res3c_branch2a (Conv2D) (None, 28, 28, 128) 65664 ____________________________________________________________________________________________________ bn3c_branch2a (BatchNormalizatio (None, 28, 28, 128) 512 ____________________________________________________________________________________________________ activation_17 (Activation) (None, 28, 28, 128) 0 ____________________________________________________________________________________________________ res3c_branch2b (Conv2D) (None, 28, 28, 128) 147584 ____________________________________________________________________________________________________ bn3c_branch2b (BatchNormalizatio (None, 28, 28, 128) 512 ____________________________________________________________________________________________________ activation_18 (Activation) (None, 28, 28, 128) 0 ____________________________________________________________________________________________________ res3c_branch2c (Conv2D) (None, 28, 28, 512) 66048 ____________________________________________________________________________________________________ bn3c_branch2c (BatchNormalizatio (None, 28, 28, 512) 2048 ____________________________________________________________________________________________________ add_6 (Add) (None, 28, 28, 512) 0 ____________________________________________________________________________________________________ activation_19 (Activation) (None, 28, 28, 512) 0 ____________________________________________________________________________________________________ res3d_branch2a (Conv2D) (None, 28, 28, 128) 65664 ____________________________________________________________________________________________________ bn3d_branch2a (BatchNormalizatio (None, 28, 28, 128) 512 ____________________________________________________________________________________________________ activation_20 (Activation) (None, 28, 28, 128) 0 ____________________________________________________________________________________________________ res3d_branch2b (Conv2D) (None, 28, 28, 128) 147584 ____________________________________________________________________________________________________ bn3d_branch2b (BatchNormalizatio (None, 28, 28, 128) 512 ____________________________________________________________________________________________________ activation_21 (Activation) (None, 28, 28, 128) 0 ____________________________________________________________________________________________________ res3d_branch2c (Conv2D) (None, 28, 28, 512) 66048 ____________________________________________________________________________________________________ bn3d_branch2c (BatchNormalizatio (None, 28, 28, 512) 2048 ____________________________________________________________________________________________________ add_7 (Add) (None, 28, 28, 512) 0 ____________________________________________________________________________________________________ activation_22 (Activation) (None, 28, 28, 512) 0 ____________________________________________________________________________________________________ res4a_branch2a (Conv2D) (None, 14, 14, 256) 131328 ____________________________________________________________________________________________________ bn4a_branch2a (BatchNormalizatio (None, 14, 14, 256) 1024 ____________________________________________________________________________________________________ activation_23 (Activation) (None, 14, 14, 256) 0 ____________________________________________________________________________________________________ res4a_branch2b (Conv2D) (None, 14, 14, 256) 590080 ____________________________________________________________________________________________________ bn4a_branch2b (BatchNormalizatio (None, 14, 14, 256) 1024 ____________________________________________________________________________________________________ activation_24 (Activation) (None, 14, 14, 256) 0 ____________________________________________________________________________________________________ res4a_branch2c (Conv2D) (None, 14, 14, 1024) 263168 ____________________________________________________________________________________________________ res4a_branch1 (Conv2D) (None, 14, 14, 1024) 525312 ____________________________________________________________________________________________________ bn4a_branch2c (BatchNormalizatio (None, 14, 14, 1024) 4096 ____________________________________________________________________________________________________ bn4a_branch1 (BatchNormalization (None, 14, 14, 1024) 4096 ____________________________________________________________________________________________________ add_8 (Add) (None, 14, 14, 1024) 0 ____________________________________________________________________________________________________ activation_25 (Activation) (None, 14, 14, 1024) 0 ____________________________________________________________________________________________________ res4b_branch2a (Conv2D) (None, 14, 14, 256) 262400 ____________________________________________________________________________________________________ bn4b_branch2a (BatchNormalizatio (None, 14, 14, 256) 1024 ____________________________________________________________________________________________________ activation_26 (Activation) (None, 14, 14, 256) 0 ____________________________________________________________________________________________________ res4b_branch2b (Conv2D) (None, 14, 14, 256) 590080 ____________________________________________________________________________________________________ bn4b_branch2b (BatchNormalizatio (None, 14, 14, 256) 1024 ____________________________________________________________________________________________________ activation_27 (Activation) (None, 14, 14, 256) 0 ____________________________________________________________________________________________________ res4b_branch2c (Conv2D) (None, 14, 14, 1024) 263168 ____________________________________________________________________________________________________ bn4b_branch2c (BatchNormalizatio (None, 14, 14, 1024) 4096 ____________________________________________________________________________________________________ add_9 (Add) (None, 14, 14, 1024) 0 ____________________________________________________________________________________________________ activation_28 (Activation) (None, 14, 14, 1024) 0 ____________________________________________________________________________________________________ res4c_branch2a (Conv2D) (None, 14, 14, 256) 262400 ____________________________________________________________________________________________________ bn4c_branch2a (BatchNormalizatio (None, 14, 14, 256) 1024 ____________________________________________________________________________________________________ activation_29 (Activation) (None, 14, 14, 256) 0 ____________________________________________________________________________________________________ res4c_branch2b (Conv2D) (None, 14, 14, 256) 590080 ____________________________________________________________________________________________________ bn4c_branch2b (BatchNormalizatio (None, 14, 14, 256) 1024 ____________________________________________________________________________________________________ activation_30 (Activation) (None, 14, 14, 256) 0 ____________________________________________________________________________________________________ res4c_branch2c (Conv2D) (None, 14, 14, 1024) 263168 ____________________________________________________________________________________________________ bn4c_branch2c (BatchNormalizatio (None, 14, 14, 1024) 4096 ____________________________________________________________________________________________________ add_10 (Add) (None, 14, 14, 1024) 0 ____________________________________________________________________________________________________ activation_31 (Activation) (None, 14, 14, 1024) 0 ____________________________________________________________________________________________________ res4d_branch2a (Conv2D) (None, 14, 14, 256) 262400 ____________________________________________________________________________________________________ bn4d_branch2a (BatchNormalizatio (None, 14, 14, 256) 1024 ____________________________________________________________________________________________________ activation_32 (Activation) (None, 14, 14, 256) 0 ____________________________________________________________________________________________________ res4d_branch2b (Conv2D) (None, 14, 14, 256) 590080 ____________________________________________________________________________________________________ bn4d_branch2b (BatchNormalizatio (None, 14, 14, 256) 1024 ____________________________________________________________________________________________________ activation_33 (Activation) (None, 14, 14, 256) 0 ____________________________________________________________________________________________________ res4d_branch2c (Conv2D) (None, 14, 14, 1024) 263168 ____________________________________________________________________________________________________ bn4d_branch2c (BatchNormalizatio (None, 14, 14, 1024) 4096 ____________________________________________________________________________________________________ add_11 (Add) (None, 14, 14, 1024) 0 ____________________________________________________________________________________________________ activation_34 (Activation) (None, 14, 14, 1024) 0 ____________________________________________________________________________________________________ res4e_branch2a (Conv2D) (None, 14, 14, 256) 262400 ____________________________________________________________________________________________________ bn4e_branch2a (BatchNormalizatio (None, 14, 14, 256) 1024 ____________________________________________________________________________________________________ activation_35 (Activation) (None, 14, 14, 256) 0 ____________________________________________________________________________________________________ res4e_branch2b (Conv2D) (None, 14, 14, 256) 590080 ____________________________________________________________________________________________________ bn4e_branch2b (BatchNormalizatio (None, 14, 14, 256) 1024 ____________________________________________________________________________________________________ activation_36 (Activation) (None, 14, 14, 256) 0 ____________________________________________________________________________________________________ res4e_branch2c (Conv2D) (None, 14, 14, 1024) 263168 ____________________________________________________________________________________________________ bn4e_branch2c (BatchNormalizatio (None, 14, 14, 1024) 4096 ____________________________________________________________________________________________________ add_12 (Add) (None, 14, 14, 1024) 0 ____________________________________________________________________________________________________ activation_37 (Activation) (None, 14, 14, 1024) 0 ____________________________________________________________________________________________________ res4f_branch2a (Conv2D) (None, 14, 14, 256) 262400 ____________________________________________________________________________________________________ bn4f_branch2a (BatchNormalizatio (None, 14, 14, 256) 1024 ____________________________________________________________________________________________________ activation_38 (Activation) (None, 14, 14, 256) 0 ____________________________________________________________________________________________________ res4f_branch2b (Conv2D) (None, 14, 14, 256) 590080 ____________________________________________________________________________________________________ bn4f_branch2b (BatchNormalizatio (None, 14, 14, 256) 1024 ____________________________________________________________________________________________________ activation_39 (Activation) (None, 14, 14, 256) 0 ____________________________________________________________________________________________________ res4f_branch2c (Conv2D) (None, 14, 14, 1024) 263168 ____________________________________________________________________________________________________ bn4f_branch2c (BatchNormalizatio (None, 14, 14, 1024) 4096 ____________________________________________________________________________________________________ add_13 (Add) (None, 14, 14, 1024) 0 ____________________________________________________________________________________________________ activation_40 (Activation) (None, 14, 14, 1024) 0 ____________________________________________________________________________________________________ res5a_branch2a (Conv2D) (None, 7, 7, 512) 524800 ____________________________________________________________________________________________________ bn5a_branch2a (BatchNormalizatio (None, 7, 7, 512) 2048 ____________________________________________________________________________________________________ activation_41 (Activation) (None, 7, 7, 512) 0 ____________________________________________________________________________________________________ res5a_branch2b (Conv2D) (None, 7, 7, 512) 2359808 ____________________________________________________________________________________________________ bn5a_branch2b (BatchNormalizatio (None, 7, 7, 512) 2048 ____________________________________________________________________________________________________ activation_42 (Activation) (None, 7, 7, 512) 0 ____________________________________________________________________________________________________ res5a_branch2c (Conv2D) (None, 7, 7, 2048) 1050624 ____________________________________________________________________________________________________ res5a_branch1 (Conv2D) (None, 7, 7, 2048) 2099200 ____________________________________________________________________________________________________ bn5a_branch2c (BatchNormalizatio (None, 7, 7, 2048) 8192 ____________________________________________________________________________________________________ bn5a_branch1 (BatchNormalization (None, 7, 7, 2048) 8192 ____________________________________________________________________________________________________ add_14 (Add) (None, 7, 7, 2048) 0 ____________________________________________________________________________________________________ activation_43 (Activation) (None, 7, 7, 2048) 0 ____________________________________________________________________________________________________ res5b_branch2a (Conv2D) (None, 7, 7, 512) 1049088 ____________________________________________________________________________________________________ bn5b_branch2a (BatchNormalizatio (None, 7, 7, 512) 2048 ____________________________________________________________________________________________________ activation_44 (Activation) (None, 7, 7, 512) 0 ____________________________________________________________________________________________________ res5b_branch2b (Conv2D) (None, 7, 7, 512) 2359808 ____________________________________________________________________________________________________ bn5b_branch2b (BatchNormalizatio (None, 7, 7, 512) 2048 ____________________________________________________________________________________________________ activation_45 (Activation) (None, 7, 7, 512) 0 ____________________________________________________________________________________________________ res5b_branch2c (Conv2D) (None, 7, 7, 2048) 1050624 ____________________________________________________________________________________________________ bn5b_branch2c (BatchNormalizatio (None, 7, 7, 2048) 8192 ____________________________________________________________________________________________________ add_15 (Add) (None, 7, 7, 2048) 0 ____________________________________________________________________________________________________ activation_46 (Activation) (None, 7, 7, 2048) 0 ____________________________________________________________________________________________________ res5c_branch2a (Conv2D) (None, 7, 7, 512) 1049088 ____________________________________________________________________________________________________ bn5c_branch2a (BatchNormalizatio (None, 7, 7, 512) 2048 ____________________________________________________________________________________________________ activation_47 (Activation) (None, 7, 7, 512) 0 ____________________________________________________________________________________________________ res5c_branch2b (Conv2D) (None, 7, 7, 512) 2359808 ____________________________________________________________________________________________________ bn5c_branch2b (BatchNormalizatio (None, 7, 7, 512) 2048 ____________________________________________________________________________________________________ activation_48 (Activation) (None, 7, 7, 512) 0 ____________________________________________________________________________________________________ res5c_branch2c (Conv2D) (None, 7, 7, 2048) 1050624 ____________________________________________________________________________________________________ bn5c_branch2c (BatchNormalizatio (None, 7, 7, 2048) 8192 ____________________________________________________________________________________________________ add_16 (Add) (None, 7, 7, 2048) 0 ____________________________________________________________________________________________________ activation_49 (Activation) (None, 7, 7, 2048) 0 ____________________________________________________________________________________________________ avg_pool (AveragePooling2D) (None, 1, 1, 2048) 0 ____________________________________________________________________________________________________ flatten_1 (Flatten) (None, 2048) 0 ____________________________________________________________________________________________________ fc1000 (Dense) (None, 1000) 2049000 ==================================================================================================== Total params: 25,636,712.0 Trainable params: 25,583,592.0 Non-trainable params: 53,120.0 ____________________________________________________________________________________________________
When using TensorFlow as backend, Keras CNNs require a 4D array (which we'll also refer to as a 4D tensor) as input, with shape
$$ (\text{nb_samples}, \text{rows}, \text{columns}, \text{channels}), $$where nb_samples corresponds to the total number of images (or samples), and rows, columns, and channels correspond to the number of rows, columns, and channels for each image, respectively.
The path_to_tensor function below takes a string-valued file path to a color image as input and returns a 4D tensor suitable for supplying to a Keras CNN. The function first loads the image and resizes it to a square image that is $224 \times 224$ pixels. Next, the image is converted to an array, which is then resized to a 4D tensor. In this case, since we are working with color images, each image has three channels. Likewise, since we are processing a single image (or sample), the returned tensor will always have shape
The paths_to_tensor function takes a numpy array of string-valued image paths as input and returns a 4D tensor with shape
Here, nb_samples is the number of samples, or number of images, in the supplied array of image paths. It is best to think of nb_samples as the number of 3D tensors (where each 3D tensor corresponds to a different image) in your dataset!
from keras.preprocessing import image
from tqdm import tqdm
def path_to_tensor(img_path):
#print(img_path)
# loads RGB image as PIL.Image.Image type
img = image.load_img(img_path, target_size=(224, 224))
# convert PIL.Image.Image type to 3D tensor with shape (224, 224, 3)
x = image.img_to_array(img)
# convert 3D tensor to 4D tensor with shape (1, 224, 224, 3) and return 4D tensor
return np.expand_dims(x, axis=0)
def paths_to_tensor(img_paths):
list_of_tensors = [path_to_tensor(img_path) for img_path in tqdm(img_paths)]
return np.vstack(list_of_tensors)
Getting the 4D tensor ready for ResNet-50, and for any other pre-trained model in Keras, requires some additional processing. First, the RGB image is converted to BGR by reordering the channels. All pre-trained models have the additional normalization step that the mean pixel (expressed in RGB as $[103.939, 116.779, 123.68]$ and calculated from all pixels in all images in ImageNet) must be subtracted from every pixel in each image. This is implemented in the imported function preprocess_input. If you're curious, you can check the code for preprocess_input here.
Now that we have a way to format our image for supplying to ResNet-50, we are now ready to use the model to extract the predictions. This is accomplished with the predict method, which returns an array whose $i$-th entry is the model's predicted probability that the image belongs to the $i$-th ImageNet category. This is implemented in the ResNet50_predict_labels function below.
By taking the argmax of the predicted probability vector, we obtain an integer corresponding to the model's predicted object class, which we can identify with an object category through the use of this dictionary.
from keras.applications.resnet50 import preprocess_input, decode_predictions
def ResNet50_predict_labels(img_path):
# returns prediction vector for image located at img_path
img = preprocess_input(path_to_tensor(img_path))
return np.argmax(ResNet50_model.predict(img))
While looking at the dictionary, you will notice that the categories corresponding to dogs appear in an uninterrupted sequence and correspond to dictionary keys 151-268, inclusive, to include all categories from 'Chihuahua' to 'Mexican hairless'. Thus, in order to check to see if an image is predicted to contain a dog by the pre-trained ResNet-50 model, we need only check if the ResNet50_predict_labels function above returns a value between 151 and 268 (inclusive).
We use these ideas to complete the dog_detector function below, which returns True if a dog is detected in an image (and False if not).
### returns "True" if a dog is detected in the image stored at img_path
def dog_detector(img_path):
prediction = ResNet50_predict_labels(img_path)
return ((prediction <= 268) & (prediction >= 151))
Question 3: Use the code cell below to test the performance of your dog_detector function.
human_files_short have a detected dog? dog_files_short have a detected dog?Answer:
Human file detected as dog is 1%
Dog file detected as dog is 100%
### TODO: Test the performance of the dog_detector function
### on the images in human_files_short and dog_files_short.
human_file_dog_count = 0
for human_file in human_files_short:
if dog_detector(human_file):
human_file_dog_count += 1
dog_file_dog_count = 0
for dog_file in dog_files_short:
if dog_detector(dog_file):
dog_file_dog_count += 1
#print('Human Face Detected out of %d files is %d ' % (len(human_files_short1), human_file_count1))
print('Human file detected as dog is %s ' % str(human_file_dog_count) )
print('Dog file detected as dog is %s ' % str(dog_file_dog_count) )
Human file detected as dog is 1 Dog file detected as dog is 100
Now that we have functions for detecting humans and dogs in images, we need a way to predict breed from images. In this step, you will create a CNN that classifies dog breeds. You must create your CNN from scratch (so, you can't use transfer learning yet!), and you must attain a test accuracy of at least 1%. In Step 5 of this notebook, you will have the opportunity to use transfer learning to create a CNN that attains greatly improved accuracy.
Be careful with adding too many trainable layers! More parameters means longer training, which means you are more likely to need a GPU to accelerate the training process. Thankfully, Keras provides a handy estimate of the time that each epoch is likely to take; you can extrapolate this estimate to figure out how long it will take for your algorithm to train.
We mention that the task of assigning breed to dogs from images is considered exceptionally challenging. To see why, consider that even a human would have great difficulty in distinguishing between a Brittany and a Welsh Springer Spaniel.
| Brittany | Welsh Springer Spaniel |
|---|---|
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It is not difficult to find other dog breed pairs with minimal inter-class variation (for instance, Curly-Coated Retrievers and American Water Spaniels).
| Curly-Coated Retriever | American Water Spaniel |
|---|---|
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Likewise, recall that labradors come in yellow, chocolate, and black. Your vision-based algorithm will have to conquer this high intra-class variation to determine how to classify all of these different shades as the same breed.
| Yellow Labrador | Chocolate Labrador | Black Labrador |
|---|---|---|
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We also mention that random chance presents an exceptionally low bar: setting aside the fact that the classes are slightly imabalanced, a random guess will provide a correct answer roughly 1 in 133 times, which corresponds to an accuracy of less than 1%.
Remember that the practice is far ahead of the theory in deep learning. Experiment with many different architectures, and trust your intuition. And, of course, have fun!
We rescale the images by dividing every pixel in every image by 255.
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
# pre-process the data for Keras
train_tensors = paths_to_tensor(train_files).astype('float32')/255
valid_tensors = paths_to_tensor(valid_files).astype('float32')/255
test_tensors = paths_to_tensor(test_files).astype('float32')/255
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6680/6680 [01:47<00:00, 61.92it/s] 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 835/835 [01:19<00:00, 10.56it/s] 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 836/836 [00:43<00:00, 19.02it/s]
Create a CNN to classify dog breed. At the end of your code cell block, summarize the layers of your model by executing the line:
model.summary()
We have imported some Python modules to get you started, but feel free to import as many modules as you need. If you end up getting stuck, here's a hint that specifies a model that trains relatively fast on CPU and attains >1% test accuracy in 5 epochs:

Question 4: Outline the steps you took to get to your final CNN architecture and your reasoning at each step. If you chose to use the hinted architecture above, describe why you think that CNN architecture should work well for the image classification task.
Answer:
CNN is a good fit for image classification, it uses deeplearning to process each lay and use it in the next layer to further enhance it. In case of CNN in the First layer edges and lines are identified in the next layer it uses these lines and edges to identify the next step that is the shapes and so on and finally the last layer identifies the details of object which in this case the eyes , teeth , face of the dog or human.
from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D
from keras.layers import Dropout, Flatten, Dense
from keras.models import Sequential
model = Sequential()
### TODO: Define your architecture.
model.add(Conv2D(filters=16, kernel_size=2, padding='same', activation='relu', input_shape=(224, 224, 3)))
model.add(MaxPooling2D(pool_size=2))
model.add(Conv2D(filters=32, kernel_size=2, padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Conv2D(filters=64, kernel_size=2, padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(GlobalAveragePooling2D(input_shape=(7, 7, 512)))
model.add(Dense(133, activation='softmax'))
model.summary()
print('Hello')
_________________________________________________________________ Layer (type) Output Shape Param # ================================================================= conv2d_1 (Conv2D) (None, 224, 224, 16) 208 _________________________________________________________________ max_pooling2d_2 (MaxPooling2 (None, 112, 112, 16) 0 _________________________________________________________________ conv2d_2 (Conv2D) (None, 112, 112, 32) 2080 _________________________________________________________________ max_pooling2d_3 (MaxPooling2 (None, 56, 56, 32) 0 _________________________________________________________________ conv2d_3 (Conv2D) (None, 56, 56, 64) 8256 _________________________________________________________________ max_pooling2d_4 (MaxPooling2 (None, 28, 28, 64) 0 _________________________________________________________________ global_average_pooling2d_1 ( (None, 64) 0 _________________________________________________________________ dense_1 (Dense) (None, 133) 8645 ================================================================= Total params: 19,189.0 Trainable params: 19,189.0 Non-trainable params: 0.0 _________________________________________________________________ Hello
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
Train your model in the code cell below. Use model checkpointing to save the model that attains the best validation loss.
You are welcome to augment the training data, but this is not a requirement.
from keras.callbacks import ModelCheckpoint
### TODO: specify the number of epochs that you would like to use to train the model.
epochs = 10
### Do NOT modify the code below this line.
checkpointer = ModelCheckpoint(filepath='weights.best.from_scratch.hdf5',
verbose=1, save_best_only=True)
model.fit(train_tensors, train_targets,
validation_data=(valid_tensors, valid_targets),
epochs=epochs, batch_size=20, callbacks=[checkpointer], verbose=1)
print('--- Sction Complete ----')
Train on 6680 samples, validate on 835 samples Epoch 1/10 6660/6680 [============================>.] - ETA: 602s - loss: 4.8835 - acc: 0.0000e+00 - ETA: 438s - loss: 4.8868 - acc: 0.0000e+00 - ETA: 378s - loss: 4.8901 - acc: 0.0000e+00 - ETA: 349s - loss: 4.8926 - acc: 0.0000e+00 - ETA: 334s - loss: 4.8928 - acc: 0.0000e+00 - ETA: 321s - loss: 4.8917 - acc: 0.0000e+00 - ETA: 314s - loss: 4.8916 - acc: 0.0000e+00 - ETA: 309s - loss: 4.8918 - acc: 0.0000e+00 - ETA: 303s - loss: 4.8912 - acc: 0.0000e+00 - ETA: 299s - loss: 4.8904 - acc: 0.0000e+00 - ETA: 295s - loss: 4.8888 - acc: 0.0000e+00 - ETA: 296s - loss: 4.8877 - acc: 0.0000e+00 - ETA: 294s - loss: 4.8894 - acc: 0.0000e+00 - ETA: 291s - loss: 4.8895 - acc: 0.0000e+00 - ETA: 289s - loss: 4.8892 - acc: 0.0000e+00 - ETA: 287s - loss: 4.8896 - acc: 0.0000e+00 - ETA: 287s - loss: 4.8905 - acc: 0.0029 - ETA: 285s - loss: 4.8900 - acc: 0.0028 - ETA: 283s - loss: 4.8894 - acc: 0.0026 - ETA: 281s - loss: 4.8901 - acc: 0.0025 - ETA: 280s - loss: 4.8898 - acc: 0.0048 - ETA: 279s - loss: 4.8895 - acc: 0.0045 - ETA: 278s - loss: 4.8887 - acc: 0.0043 - ETA: 277s - loss: 4.8886 - acc: 0.0042 - ETA: 276s - loss: 4.8883 - acc: 0.0040 - ETA: 275s - loss: 4.8881 - acc: 0.0077 - ETA: 274s - loss: 4.8885 - acc: 0.0074 - ETA: 274s - loss: 4.8884 - acc: 0.0071 - ETA: 272s - loss: 4.8864 - acc: 0.0069 - ETA: 271s - loss: 4.8869 - acc: 0.0067 - ETA: 271s - loss: 4.8872 - acc: 0.0065 - ETA: 270s - loss: 4.8871 - acc: 0.0078 - ETA: 269s - loss: 4.8862 - acc: 0.0076 - ETA: 269s - loss: 4.8862 - acc: 0.0074 - ETA: 268s - loss: 4.8851 - acc: 0.0071 - ETA: 268s - loss: 4.8855 - acc: 0.0069 - ETA: 267s - loss: 4.8859 - acc: 0.0068 - ETA: 267s - loss: 4.8864 - acc: 0.0066 - ETA: 266s - loss: 4.8862 - acc: 0.0064 - ETA: 265s - loss: 4.8867 - acc: 0.0063 - ETA: 265s - loss: 4.8866 - acc: 0.0073 - ETA: 264s - loss: 4.8870 - acc: 0.0071 - ETA: 263s - loss: 4.8877 - acc: 0.0070 - ETA: 262s - loss: 4.8876 - acc: 0.0068 - ETA: 261s - loss: 4.8886 - acc: 0.0067 - ETA: 260s - loss: 4.8888 - acc: 0.0065 - ETA: 260s - loss: 4.8886 - acc: 0.0064 - ETA: 259s - loss: 4.8890 - acc: 0.0063 - ETA: 258s - loss: 4.8894 - acc: 0.0061 - ETA: 257s - loss: 4.8901 - acc: 0.0060 - ETA: 256s - loss: 4.8902 - acc: 0.0069 - ETA: 255s - loss: 4.8906 - acc: 0.0067 - ETA: 254s - loss: 4.8910 - acc: 0.0066 - ETA: 253s - loss: 4.8910 - acc: 0.0065 - ETA: 252s - loss: 4.8908 - acc: 0.0064 - ETA: 251s - loss: 4.8909 - acc: 0.0063 - ETA: 250s - loss: 4.8909 - acc: 0.0061 - ETA: 249s - loss: 4.8908 - acc: 0.0060 - ETA: 248s - loss: 4.8909 - acc: 0.0059 - ETA: 247s - loss: 4.8907 - acc: 0.0058 - ETA: 247s - loss: 4.8906 - acc: 0.0057 - ETA: 246s - loss: 4.8908 - acc: 0.0056 - ETA: 245s - loss: 4.8908 - acc: 0.0056 - ETA: 245s - loss: 4.8906 - acc: 0.0055 - ETA: 244s - loss: 4.8906 - acc: 0.0062 - ETA: 243s - loss: 4.8907 - acc: 0.0061 - ETA: 242s - loss: 4.8907 - acc: 0.0060 - ETA: 242s - loss: 4.8907 - acc: 0.0059 - ETA: 241s - loss: 4.8906 - acc: 0.0058 - ETA: 240s - loss: 4.8908 - acc: 0.0057 - ETA: 239s - loss: 4.8910 - acc: 0.0056 - ETA: 238s - loss: 4.8908 - acc: 0.0069 - ETA: 237s - loss: 4.8908 - acc: 0.0068 - ETA: 236s - loss: 4.8909 - acc: 0.0068 - ETA: 235s - loss: 4.8908 - acc: 0.0067 - ETA: 234s - loss: 4.8908 - acc: 0.0066 - ETA: 233s - loss: 4.8905 - acc: 0.0065 - ETA: 232s - loss: 4.8901 - acc: 0.0071 - ETA: 232s - loss: 4.8902 - acc: 0.0070 - ETA: 231s - loss: 4.8903 - acc: 0.0069 - ETA: 230s - loss: 4.8905 - acc: 0.0068 - ETA: 229s - loss: 4.8906 - acc: 0.0067 - ETA: 228s - loss: 4.8905 - acc: 0.0072 - ETA: 227s - loss: 4.8905 - acc: 0.0077 - ETA: 227s - loss: 4.8904 - acc: 0.0076 - ETA: 226s - loss: 4.8903 - acc: 0.0076 - ETA: 225s - loss: 4.8904 - acc: 0.0075 - ETA: 224s - loss: 4.8905 - acc: 0.0074 - ETA: 223s - loss: 4.8904 - acc: 0.0073 - ETA: 223s - loss: 4.8904 - acc: 0.0072 - ETA: 222s - loss: 4.8904 - acc: 0.0071 - ETA: 221s - loss: 4.8904 - acc: 0.0076 - ETA: 220s - loss: 4.8906 - acc: 0.0081 - ETA: 219s - loss: 4.8904 - acc: 0.0080 - ETA: 218s - loss: 4.8902 - 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314s - loss: 4.8490 - acc: 0.0144 - val_loss: 4.8489 - val_acc: 0.0132 Epoch 4/10 6660/6680 [============================>.] - ETA: 295s - loss: 4.8374 - acc: 0.0000e+00 - ETA: 298s - loss: 4.8054 - acc: 0.0000e+00 - ETA: 298s - loss: 4.7965 - acc: 0.0167 - ETA: 297s - loss: 4.7989 - acc: 0.0125 - ETA: 295s - loss: 4.8090 - acc: 0.0100 - ETA: 295s - loss: 4.8145 - acc: 0.0083 - ETA: 294s - loss: 4.8217 - acc: 0.0071 - ETA: 293s - loss: 4.8243 - acc: 0.0063 - ETA: 292s - loss: 4.8290 - acc: 0.0056 - ETA: 292s - loss: 4.8212 - acc: 0.0050 - ETA: 291s - loss: 4.8233 - acc: 0.0045 - ETA: 290s - loss: 4.8160 - acc: 0.0083 - ETA: 289s - loss: 4.8146 - acc: 0.0077 - ETA: 288s - loss: 4.8114 - acc: 0.0071 - ETA: 286s - loss: 4.8197 - acc: 0.0067 - ETA: 285s - loss: 4.8193 - acc: 0.0063 - ETA: 285s - loss: 4.8168 - acc: 0.0088 - ETA: 284s - loss: 4.8110 - acc: 0.0083 - ETA: 283s - loss: 4.8112 - acc: 0.0079 - ETA: 282s - loss: 4.8083 - acc: 0.0100 - ETA: 281s - loss: 4.8020 - acc: 0.0167 - ETA: 280s - 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321s - loss: 4.7444 - acc: 0.0225 - val_loss: 4.7621 - val_acc: 0.0216 Epoch 7/10 6660/6680 [============================>.] - ETA: 303s - loss: 4.7271 - acc: 0.0000e+00 - ETA: 296s - loss: 4.7373 - acc: 0.0000e+00 - ETA: 295s - loss: 4.7169 - acc: 0.0000e+00 - ETA: 295s - loss: 4.7495 - acc: 0.0000e+00 - ETA: 295s - loss: 4.6926 - acc: 0.0100 - ETA: 293s - loss: 4.6810 - acc: 0.0167 - ETA: 292s - loss: 4.7219 - acc: 0.0214 - ETA: 291s - loss: 4.7172 - acc: 0.0313 - ETA: 289s - loss: 4.7293 - acc: 0.0278 - ETA: 288s - loss: 4.7239 - acc: 0.0250 - ETA: 288s - loss: 4.7109 - acc: 0.0227 - ETA: 287s - loss: 4.7035 - acc: 0.0208 - ETA: 286s - loss: 4.7109 - acc: 0.0231 - ETA: 286s - loss: 4.7202 - acc: 0.0214 - ETA: 285s - loss: 4.7224 - acc: 0.0233 - ETA: 285s - loss: 4.7086 - acc: 0.0250 - ETA: 283s - loss: 4.7151 - acc: 0.0235 - ETA: 283s - loss: 4.7086 - acc: 0.0250 - ETA: 282s - loss: 4.7157 - acc: 0.0237 - ETA: 281s - loss: 4.7197 - acc: 0.0250 - ETA: 281s - loss: 4.7250 - acc: 0.0238 - 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315s - loss: 4.6552 - acc: 0.0338 - val_loss: 4.6942 - val_acc: 0.0287 Epoch 10/10 6660/6680 [============================>.] - ETA: 291s - loss: 4.4873 - acc: 0.0000e+00 - ETA: 296s - loss: 4.5930 - acc: 0.0250 - ETA: 297s - loss: 4.5695 - acc: 0.0333 - ETA: 297s - loss: 4.5928 - acc: 0.0375 - ETA: 297s - loss: 4.6122 - acc: 0.0300 - ETA: 297s - loss: 4.6044 - acc: 0.0333 - ETA: 295s - loss: 4.6896 - acc: 0.0357 - ETA: 293s - loss: 4.6965 - acc: 0.0375 - ETA: 291s - loss: 4.6867 - acc: 0.0333 - ETA: 291s - loss: 4.6630 - acc: 0.0400 - ETA: 290s - loss: 4.6798 - acc: 0.0364 - ETA: 289s - loss: 4.6636 - acc: 0.0417 - ETA: 288s - loss: 4.6496 - acc: 0.0423 - ETA: 288s - loss: 4.6581 - acc: 0.0393 - ETA: 287s - loss: 4.6546 - acc: 0.0400 - ETA: 286s - loss: 4.6509 - acc: 0.0406 - ETA: 285s - loss: 4.6486 - acc: 0.0382 - ETA: 284s - loss: 4.6469 - acc: 0.0389 - ETA: 284s - loss: 4.6533 - acc: 0.0368 - ETA: 283s - loss: 4.6585 - acc: 0.0400 - ETA: 282s - loss: 4.6487 - acc: 0.0429 - ETA: 281s - 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acc: 0.0364 - ETA: 57s - loss: 4.6210 - acc: 0.0365 - ETA: 56s - loss: 4.6218 - acc: 0.0363 - ETA: 55s - loss: 4.6214 - acc: 0.0364 - ETA: 54s - loss: 4.6215 - acc: 0.0364 - ETA: 53s - loss: 4.6210 - acc: 0.0363 - ETA: 52s - loss: 4.6205 - acc: 0.0364 - ETA: 52s - loss: 4.6197 - acc: 0.0364 - ETA: 51s - loss: 4.6196 - acc: 0.0365 - ETA: 50s - loss: 4.6199 - acc: 0.0363 - ETA: 49s - loss: 4.6197 - acc: 0.0362 - ETA: 48s - loss: 4.6194 - acc: 0.0361 - ETA: 47s - loss: 4.6194 - acc: 0.0361 - ETA: 46s - loss: 4.6183 - acc: 0.0360 - ETA: 45s - loss: 4.6179 - acc: 0.0359 - ETA: 44s - loss: 4.6175 - acc: 0.0363 - ETA: 43s - loss: 4.6172 - acc: 0.0363 - ETA: 43s - loss: 4.6191 - acc: 0.0362 - ETA: 42s - loss: 4.6191 - acc: 0.0361 - ETA: 41s - loss: 4.6201 - acc: 0.0359 - ETA: 40s - loss: 4.6202 - acc: 0.0358 - ETA: 39s - loss: 4.6194 - acc: 0.0357 - ETA: 38s - loss: 4.6201 - acc: 0.0356 - ETA: 37s - loss: 4.6200 - acc: 0.0354 - ETA: 36s - loss: 4.6209 - acc: 0.0355 - ETA: 35s - loss: 4.6207 - acc: 0.0355 - ETA: 34s - loss: 4.6211 - acc: 0.0356 - ETA: 34s - loss: 4.6210 - acc: 0.0358 - ETA: 33s - loss: 4.6214 - acc: 0.0360 - ETA: 32s - loss: 4.6216 - acc: 0.0359 - ETA: 31s - loss: 4.6219 - acc: 0.0358 - ETA: 30s - loss: 4.6220 - acc: 0.0360 - ETA: 29s - loss: 4.6212 - acc: 0.0360 - ETA: 28s - loss: 4.6214 - acc: 0.0359 - ETA: 27s - loss: 4.6216 - acc: 0.0360 - ETA: 26s - loss: 4.6226 - acc: 0.0359 - ETA: 26s - loss: 4.6228 - acc: 0.0359 - ETA: 25s - loss: 4.6227 - acc: 0.0358 - ETA: 24s - loss: 4.6225 - acc: 0.0358 - ETA: 23s - loss: 4.6230 - acc: 0.0357 - ETA: 22s - loss: 4.6224 - acc: 0.0359 - ETA: 21s - loss: 4.6219 - acc: 0.0361 - ETA: 20s - loss: 4.6227 - acc: 0.0360 - ETA: 19s - loss: 4.6235 - acc: 0.0359 - ETA: 18s - loss: 4.6236 - acc: 0.0358 - ETA: 17s - loss: 4.6234 - acc: 0.0358 - ETA: 17s - loss: 4.6230 - acc: 0.0357 - ETA: 16s - loss: 4.6229 - acc: 0.0356 - ETA: 15s - loss: 4.6221 - acc: 0.0356 - ETA: 14s - loss: 4.6217 - acc: 0.0357 - ETA: 13s - loss: 4.6214 - acc: 0.0356 - ETA: 12s - loss: 4.6209 - acc: 0.0356 - ETA: 11s - loss: 4.6211 - acc: 0.0357 - ETA: 10s - loss: 4.6218 - acc: 0.0357 - ETA: 9s - loss: 4.6222 - acc: 0.0358 - ETA: 8s - loss: 4.6219 - acc: 0.0356 - ETA: 8s - loss: 4.6220 - acc: 0.0357 - ETA: 7s - loss: 4.6211 - acc: 0.0357 - ETA: 6s - loss: 4.6215 - acc: 0.0356 - ETA: 5s - loss: 4.6223 - acc: 0.0355 - ETA: 4s - loss: 4.6228 - acc: 0.0356 - ETA: 3s - loss: 4.6223 - acc: 0.0356 - ETA: 2s - loss: 4.6223 - acc: 0.0356 - ETA: 1s - loss: 4.6225 - acc: 0.0355 - ETA: 0s - loss: 4.6228 - acc: 0.0354Epoch 00009: val_loss improved from 4.69424 to 4.67962, saving model to weights.best.from_scratch.hdf5 6680/6680 [==============================] - 315s - loss: 4.6223 - acc: 0.0355 - val_loss: 4.6796 - val_acc: 0.0347 --- Sction Complete ----
model.load_weights('weights.best.from_scratch.hdf5')
Try out your model on the test dataset of dog images. Ensure that your test accuracy is greater than 1%.
# get index of predicted dog breed for each image in test set
dog_breed_predictions = [np.argmax(model.predict(np.expand_dims(tensor, axis=0))) for tensor in test_tensors]
# report test accuracy
test_accuracy = 100*np.sum(np.array(dog_breed_predictions)==np.argmax(test_targets, axis=1))/len(dog_breed_predictions)
print('Test accuracy: %.4f%%' % test_accuracy)
Test accuracy: 3.3493%
bottleneck_features = np.load('C:/Training/udacity/AI_NanoDegree/Term2/3.Convolutional Neural Networks Videos/datasets/DogVGG16Data.npz')
#bottleneck_features = np.load('bottleneck_features/DogVGG16Data.npz')
train_VGG16 = bottleneck_features['train']
valid_VGG16 = bottleneck_features['valid']
test_VGG16 = bottleneck_features['test']
The model uses the the pre-trained VGG-16 model as a fixed feature extractor, where the last convolutional output of VGG-16 is fed as input to our model. We only add a global average pooling layer and a fully connected layer, where the latter contains one node for each dog category and is equipped with a softmax.
VGG16_model = Sequential()
VGG16_model.add(GlobalAveragePooling2D(input_shape=train_VGG16.shape[1:]))
VGG16_model.add(Dense(133, activation='softmax'))
VGG16_model.summary()
_________________________________________________________________ Layer (type) Output Shape Param # ================================================================= global_average_pooling2d_2 ( (None, 512) 0 _________________________________________________________________ dense_2 (Dense) (None, 133) 68229 ================================================================= Total params: 68,229.0 Trainable params: 68,229.0 Non-trainable params: 0.0 _________________________________________________________________
VGG16_model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
checkpointer = ModelCheckpoint(filepath='weights.best.VGG16.hdf5',
verbose=1, save_best_only=True)
VGG16_model.fit(train_VGG16, train_targets,
validation_data=(valid_VGG16, valid_targets),
epochs=20, batch_size=20, callbacks=[checkpointer], verbose=1)
print('---I am done saving model VGG16----')
Train on 6680 samples, validate on 835 samples Epoch 1/20 6640/6680 [============================>.] - ETA: 225s - loss: 16.1181 - acc: 0.0000e+00 - ETA: 48s - loss: 15.0262 - acc: 0.0100 - ETA: 21s - loss: 14.9445 - acc: 0.0292 - ETA: 14s - loss: 14.6817 - acc: 0.0306 - ETA: 11s - loss: 14.7135 - acc: 0.0220 - ETA: 8s - loss: 14.6312 - acc: 0.0250 - ETA: 7s - loss: 14.5570 - acc: 0.0275 - ETA: 6s - loss: 14.3615 - acc: 0.0330 - ETA: 5s - loss: 14.3363 - acc: 0.0345 - ETA: 5s - loss: 14.1580 - acc: 0.0371 - ETA: 4s - loss: 14.1395 - acc: 0.0391 - ETA: 4s - loss: 14.1271 - acc: 0.0395 - ETA: 3s - loss: 14.0813 - acc: 0.0422 - ETA: 3s - loss: 13.9699 - acc: 0.0456 - ETA: 3s - loss: 13.9455 - acc: 0.0480 - ETA: 3s - loss: 13.8679 - acc: 0.0505 - ETA: 2s - loss: 13.7236 - acc: 0.0561 - ETA: 2s - loss: 13.6687 - acc: 0.0574 - ETA: 2s - loss: 13.5712 - acc: 0.0620 - ETA: 2s - loss: 13.5113 - acc: 0.0633 - ETA: 2s - loss: 13.4529 - acc: 0.0641 - ETA: 2s - loss: 13.3811 - acc: 0.0681 - ETA: 2s - loss: 13.3432 - acc: 0.0702 - ETA: 1s - loss: 13.3195 - acc: 0.0715 - ETA: 1s - loss: 13.2284 - acc: 0.0759 - ETA: 1s - loss: 13.1327 - acc: 0.0794 - ETA: 1s - loss: 13.0354 - acc: 0.0829 - ETA: 1s - loss: 12.9612 - acc: 0.0856 - ETA: 1s - loss: 12.9408 - acc: 0.0856 - ETA: 1s - loss: 12.8897 - acc: 0.0878 - ETA: 1s - loss: 12.8116 - acc: 0.0915 - ETA: 1s - loss: 12.7720 - acc: 0.0934 - ETA: 1s - loss: 12.7288 - acc: 0.0962 - ETA: 1s - loss: 12.6966 - acc: 0.0985 - ETA: 0s - loss: 12.6478 - acc: 0.1017 - ETA: 0s - loss: 12.6059 - acc: 0.1040 - ETA: 0s - loss: 12.5766 - acc: 0.1051 - ETA: 0s - loss: 12.5274 - acc: 0.1079 - ETA: 0s - loss: 12.4939 - acc: 0.1101 - ETA: 0s - loss: 12.4546 - acc: 0.1118 - ETA: 0s - loss: 12.4258 - acc: 0.1147 - ETA: 0s - loss: 12.3965 - acc: 0.1170 - ETA: 0s - loss: 12.3760 - acc: 0.1178 - ETA: 0s - loss: 12.3278 - acc: 0.1215 - ETA: 0s - loss: 12.2942 - acc: 0.1230 - ETA: 0s - loss: 12.2537 - acc: 0.1252 - ETA: 0s - loss: 12.2081 - acc: 0.1275 - ETA: 0s - loss: 12.1748 - acc: 0.1298Epoch 00000: val_loss improved from inf to 10.54641, saving model to weights.best.VGG16.hdf5 6680/6680 [==============================] - 3s - loss: 12.1716 - acc: 0.1301 - val_loss: 10.5464 - val_acc: 0.2347 Epoch 2/20 6620/6680 [============================>.] - ETA: 2s - loss: 9.7809 - acc: 0.3500 - ETA: 2s - loss: 10.1517 - acc: 0.2778 - ETA: 2s - loss: 10.6195 - acc: 0.2441 - ETA: 2s - loss: 10.3248 - acc: 0.2580 - ETA: 2s - loss: 10.4067 - acc: 0.2531 - ETA: 2s - loss: 10.3106 - acc: 0.2600 - ETA: 1s - loss: 10.1721 - acc: 0.2649 - ETA: 1s - loss: 10.1485 - acc: 0.2655 - ETA: 1s - loss: 10.1540 - acc: 0.2635 - ETA: 1s - loss: 10.1622 - acc: 0.2621 - ETA: 1s - loss: 10.0949 - acc: 0.2688 - ETA: 1s - loss: 10.1622 - acc: 0.2659 - ETA: 1s - loss: 10.1103 - acc: 0.2672 - ETA: 1s - loss: 10.1752 - acc: 0.2645 - ETA: 1s - loss: 10.1431 - acc: 0.2671 - ETA: 1s - loss: 10.1451 - acc: 0.2661 - ETA: 1s - loss: 10.1443 - acc: 0.2650 - ETA: 1s - loss: 10.1202 - acc: 0.2673 - ETA: 1s - loss: 10.1176 - acc: 0.2690 - ETA: 1s - loss: 10.1507 - acc: 0.2686 - ETA: 1s - loss: 10.1555 - acc: 0.2697 - ETA: 1s - loss: 10.1494 - acc: 0.2711 - ETA: 1s - loss: 10.1079 - acc: 0.2747 - ETA: 1s - loss: 10.1084 - acc: 0.2749 - ETA: 1s - loss: 10.0884 - acc: 0.2773 - ETA: 1s - loss: 10.1189 - acc: 0.2767 - ETA: 0s - loss: 10.1247 - acc: 0.2783 - ETA: 0s - loss: 10.1228 - acc: 0.2783 - ETA: 0s - loss: 10.0956 - acc: 0.2805 - ETA: 0s - loss: 10.1012 - acc: 0.2813 - ETA: 0s - loss: 10.0860 - acc: 0.2820 - ETA: 0s - loss: 10.0923 - acc: 0.2817 - ETA: 0s - loss: 10.0852 - acc: 0.2824 - ETA: 0s - loss: 10.0686 - acc: 0.2830 - ETA: 0s - loss: 10.0529 - acc: 0.2833 - ETA: 0s - loss: 10.0288 - acc: 0.2852 - ETA: 0s - loss: 10.0115 - acc: 0.2864 - ETA: 0s - loss: 10.0229 - acc: 0.2860 - ETA: 0s - loss: 10.0165 - acc: 0.2874 - ETA: 0s - loss: 10.0095 - acc: 0.2881 - ETA: 0s - loss: 9.9990 - acc: 0.2892 - ETA: 0s - loss: 9.9969 - acc: 0.2891 - ETA: 0s - loss: 10.0016 - acc: 0.2890 - ETA: 0s - loss: 9.9971 - acc: 0.2894 - ETA: 0s - loss: 9.9874 - acc: 0.2898 - ETA: 0s - loss: 9.9926 - acc: 0.2899Epoch 00001: val_loss improved from 10.54641 to 9.91412, saving model to weights.best.VGG16.hdf5 6680/6680 [==============================] - 2s - loss: 10.0018 - acc: 0.2898 - val_loss: 9.9141 - val_acc: 0.3030 Epoch 3/20 6600/6680 [============================>.] - ETA: 2s - loss: 8.7459 - acc: 0.4000 - ETA: 2s - loss: 8.9237 - acc: 0.3778 - ETA: 2s - loss: 9.3131 - acc: 0.3563 - ETA: 2s - loss: 9.2465 - acc: 0.3563 - ETA: 2s - loss: 9.1970 - acc: 0.3629 - ETA: 2s - loss: 9.2020 - acc: 0.3658 - ETA: 2s - loss: 9.2076 - acc: 0.3667 - ETA: 2s - loss: 9.1910 - acc: 0.3625 - ETA: 1s - loss: 9.1423 - acc: 0.3678 - ETA: 1s - loss: 9.2315 - acc: 0.3636 - ETA: 1s - loss: 9.1759 - acc: 0.3712 - ETA: 1s - loss: 9.2468 - acc: 0.3688 - ETA: 1s - loss: 9.1948 - acc: 0.3736 - ETA: 1s - loss: 9.2621 - acc: 0.3707 - ETA: 1s - loss: 9.2821 - acc: 0.3678 - ETA: 1s - loss: 9.3196 - acc: 0.3644 - ETA: 1s - loss: 9.3868 - acc: 0.3600 - ETA: 1s - loss: 9.4213 - acc: 0.3590 - ETA: 1s - loss: 9.3617 - acc: 0.3640 - ETA: 1s - loss: 9.4335 - acc: 0.3599 - ETA: 1s - loss: 9.4626 - acc: 0.3591 - ETA: 1s - loss: 9.4874 - acc: 0.3580 - ETA: 1s - loss: 9.5176 - acc: 0.3567 - ETA: 1s - loss: 9.4938 - acc: 0.3591 - ETA: 1s - loss: 9.5018 - acc: 0.3602 - ETA: 1s - loss: 9.4831 - acc: 0.3601 - ETA: 1s - loss: 9.4974 - acc: 0.3592 - ETA: 1s - loss: 9.5030 - acc: 0.3583 - ETA: 0s - loss: 9.4983 - acc: 0.3595 - ETA: 0s - loss: 9.5078 - acc: 0.3585 - ETA: 0s - loss: 9.4814 - acc: 0.3606 - ETA: 0s - loss: 9.4706 - acc: 0.3609 - ETA: 0s - loss: 9.4575 - acc: 0.3610 - ETA: 0s - loss: 9.4532 - acc: 0.3615 - ETA: 0s - loss: 9.4450 - acc: 0.3624 - ETA: 0s - loss: 9.4591 - acc: 0.3618 - ETA: 0s - loss: 9.4498 - acc: 0.3621 - ETA: 0s - loss: 9.4496 - acc: 0.3613 - ETA: 0s - loss: 9.4496 - acc: 0.3601 - ETA: 0s - loss: 9.4429 - acc: 0.3605 - ETA: 0s - loss: 9.4510 - acc: 0.3591 - ETA: 0s - loss: 9.4512 - acc: 0.3590 - ETA: 0s - loss: 9.4561 - acc: 0.3590 - ETA: 0s - loss: 9.4577 - acc: 0.3594 - ETA: 0s - loss: 9.4217 - acc: 0.3621 - ETA: 0s - loss: 9.4107 - acc: 0.3625 - ETA: 0s - loss: 9.4101 - acc: 0.3629Epoch 00002: val_loss improved from 9.91412 to 9.51400, saving model to weights.best.VGG16.hdf5 6680/6680 [==============================] - 2s - loss: 9.4291 - acc: 0.3620 - val_loss: 9.5140 - val_acc: 0.3413 Epoch 4/20 6620/6680 [============================>.] - ETA: 2s - loss: 9.1227 - acc: 0.4000 - ETA: 2s - loss: 9.9239 - acc: 0.3625 - ETA: 2s - loss: 9.6257 - acc: 0.3700 - ETA: 2s - loss: 9.8077 - acc: 0.3591 - ETA: 2s - loss: 9.4547 - acc: 0.3741 - ETA: 2s - loss: 9.0714 - acc: 0.3958 - ETA: 2s - loss: 9.2616 - acc: 0.3872 - ETA: 2s - loss: 9.1280 - acc: 0.3940 - ETA: 2s - loss: 9.0648 - acc: 0.4000 - ETA: 1s - loss: 9.0857 - acc: 0.3984 - ETA: 1s - loss: 9.1321 - acc: 0.3958 - ETA: 1s - loss: 9.1310 - acc: 0.3962 - ETA: 1s - loss: 9.1157 - acc: 0.3977 - ETA: 1s - loss: 9.0406 - acc: 0.4005 - 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acc: 0.3982 - ETA: 0s - loss: 9.0169 - acc: 0.3991 - ETA: 0s - loss: 9.0084 - acc: 0.3995 - ETA: 0s - loss: 9.0164 - acc: 0.3990 - ETA: 0s - loss: 9.0247 - acc: 0.3984 - ETA: 0s - loss: 9.0267 - acc: 0.3983 - ETA: 0s - loss: 9.0413 - acc: 0.3969 - ETA: 0s - loss: 9.0257 - acc: 0.3977Epoch 00003: val_loss improved from 9.51400 to 9.34174, saving model to weights.best.VGG16.hdf5 6680/6680 [==============================] - 2s - loss: 9.0333 - acc: 0.3975 - val_loss: 9.3417 - val_acc: 0.3485 Epoch 5/20 6540/6680 [============================>.] - ETA: 2s - loss: 10.4795 - acc: 0.3500 - ETA: 2s - loss: 9.6925 - acc: 0.3625 - ETA: 2s - loss: 9.6189 - acc: 0.3733 - ETA: 2s - loss: 9.5894 - acc: 0.3727 - ETA: 2s - loss: 9.4211 - acc: 0.3879 - ETA: 2s - loss: 9.4404 - acc: 0.3861 - ETA: 2s - loss: 9.3930 - acc: 0.3849 - ETA: 2s - loss: 9.4322 - acc: 0.3830 - ETA: 2s - loss: 9.4299 - acc: 0.3825 - ETA: 1s - loss: 9.3453 - acc: 0.3862 - ETA: 1s - loss: 9.2830 - acc: 0.3889 - ETA: 1s - loss: 9.2209 - acc: 0.3930 - ETA: 1s - loss: 9.2720 - acc: 0.3913 - ETA: 1s - loss: 9.3131 - acc: 0.3887 - ETA: 1s - loss: 9.2573 - acc: 0.3910 - ETA: 1s - loss: 9.2667 - acc: 0.3897 - ETA: 1s - loss: 9.1791 - acc: 0.3948 - ETA: 1s - loss: 9.1277 - acc: 0.3988 - ETA: 1s - loss: 9.1248 - acc: 0.4000 - ETA: 1s - loss: 9.0966 - acc: 0.4022 - ETA: 1s - loss: 9.0828 - acc: 0.4038 - ETA: 1s - loss: 9.0740 - acc: 0.4043 - ETA: 1s - loss: 9.0640 - acc: 0.4045 - ETA: 1s - loss: 9.0613 - acc: 0.4055 - ETA: 1s - loss: 8.9994 - acc: 0.4096 - ETA: 1s - loss: 8.9648 - acc: 0.4118 - ETA: 1s - loss: 8.9740 - acc: 0.4110 - ETA: 1s - loss: 8.9920 - acc: 0.4096 - ETA: 0s - loss: 8.9947 - acc: 0.4098 - ETA: 0s - loss: 8.9569 - acc: 0.4118 - ETA: 0s - loss: 8.9485 - acc: 0.4129 - ETA: 0s - loss: 9.0008 - acc: 0.4100 - ETA: 0s - loss: 9.0113 - acc: 0.4088 - ETA: 0s - loss: 8.9714 - acc: 0.4117 - ETA: 0s - loss: 8.9512 - acc: 0.4132 - ETA: 0s - loss: 8.9230 - acc: 0.4146 - ETA: 0s - loss: 8.9110 - acc: 0.4154 - ETA: 0s - loss: 8.9015 - acc: 0.4159 - ETA: 0s - loss: 8.9146 - acc: 0.4144 - ETA: 0s - loss: 8.9041 - acc: 0.4147 - ETA: 0s - loss: 8.8894 - acc: 0.4149 - ETA: 0s - loss: 8.8951 - acc: 0.4151 - ETA: 0s - loss: 8.8548 - acc: 0.4176 - ETA: 0s - loss: 8.8492 - acc: 0.4181 - ETA: 0s - loss: 8.8492 - acc: 0.4177 - ETA: 0s - loss: 8.8402 - acc: 0.4181 - ETA: 0s - loss: 8.8476 - acc: 0.4176Epoch 00004: val_loss improved from 9.34174 to 9.23094, saving model to weights.best.VGG16.hdf5 6680/6680 [==============================] - 2s - loss: 8.8540 - acc: 0.4175 - val_loss: 9.2309 - val_acc: 0.3629 Epoch 6/20 6660/6680 [============================>.] - ETA: 2s - loss: 9.1317 - acc: 0.4000 - ETA: 2s - loss: 9.3978 - acc: 0.3889 - ETA: 2s - loss: 8.9797 - acc: 0.4219 - ETA: 2s - loss: 8.3174 - acc: 0.4630 - ETA: 2s - loss: 8.2879 - acc: 0.4667 - ETA: 2s - loss: 8.4755 - acc: 0.4541 - ETA: 2s - loss: 8.4714 - acc: 0.4511 - ETA: 2s - loss: 8.3788 - acc: 0.4567 - ETA: 2s - loss: 8.3710 - acc: 0.4585 - ETA: 1s - loss: 8.6122 - acc: 0.4439 - ETA: 1s - loss: 8.7024 - acc: 0.4375 - ETA: 1s - loss: 8.7821 - acc: 0.4323 - ETA: 1s - loss: 8.8336 - acc: 0.4291 - ETA: 1s - loss: 8.7846 - acc: 0.4333 - ETA: 1s - loss: 8.7559 - acc: 0.4365 - ETA: 1s - loss: 8.7002 - acc: 0.4389 - ETA: 1s - loss: 8.7198 - acc: 0.4365 - ETA: 1s - loss: 8.7393 - acc: 0.4361 - ETA: 1s - loss: 8.7603 - acc: 0.4341 - ETA: 1s - loss: 8.7376 - acc: 0.4346 - ETA: 1s - loss: 8.7551 - acc: 0.4332 - ETA: 1s - loss: 8.7736 - acc: 0.4323 - ETA: 1s - loss: 8.7633 - acc: 0.4328 - ETA: 1s - loss: 8.7932 - acc: 0.4299 - ETA: 1s - loss: 8.8308 - acc: 0.4272 - ETA: 1s - loss: 8.8586 - acc: 0.4258 - ETA: 1s - loss: 8.8652 - acc: 0.4257 - ETA: 1s - loss: 8.8310 - acc: 0.4276 - ETA: 0s - loss: 8.8431 - acc: 0.4264 - ETA: 0s - loss: 8.8201 - acc: 0.4276 - ETA: 0s - loss: 8.8036 - acc: 0.4292 - ETA: 0s - loss: 8.7972 - acc: 0.4295 - ETA: 0s - loss: 8.7743 - acc: 0.4294 - ETA: 0s - loss: 8.7996 - acc: 0.4273 - ETA: 0s - loss: 8.8231 - acc: 0.4260 - ETA: 0s - loss: 8.8095 - acc: 0.4271 - ETA: 0s - loss: 8.7942 - acc: 0.4280 - ETA: 0s - loss: 8.7834 - acc: 0.4290 - ETA: 0s - loss: 8.7531 - acc: 0.4316 - ETA: 0s - loss: 8.7626 - acc: 0.4310 - ETA: 0s - loss: 8.7823 - acc: 0.4300 - ETA: 0s - loss: 8.7505 - acc: 0.4316 - ETA: 0s - loss: 8.7454 - acc: 0.4319 - ETA: 0s - loss: 8.7455 - acc: 0.4320 - ETA: 0s - loss: 8.7281 - acc: 0.4329 - ETA: 0s - loss: 8.7173 - acc: 0.4337 - ETA: 0s - loss: 8.7216 - acc: 0.4331 - ETA: 0s - loss: 8.7237 - acc: 0.4330Epoch 00005: val_loss improved from 9.23094 to 9.13905, saving model to weights.best.VGG16.hdf5 6680/6680 [==============================] - 2s - loss: 8.7253 - acc: 0.4329 - val_loss: 9.1390 - val_acc: 0.3617 Epoch 7/20 6520/6680 [============================>.] - ETA: 2s - loss: 5.9485 - acc: 0.6000 - ETA: 2s - loss: 7.5389 - acc: 0.4944 - ETA: 2s - loss: 7.5252 - acc: 0.5000 - ETA: 2s - loss: 8.0882 - acc: 0.4696 - ETA: 2s - loss: 8.2535 - acc: 0.4617 - ETA: 2s - loss: 8.1455 - acc: 0.4689 - ETA: 2s - loss: 8.1898 - acc: 0.4636 - ETA: 2s - loss: 8.1081 - acc: 0.4706 - ETA: 2s - loss: 8.2376 - acc: 0.4647 - ETA: 1s - loss: 8.2751 - acc: 0.4623 - ETA: 1s - loss: 8.2879 - acc: 0.4625 - ETA: 1s - loss: 8.2955 - acc: 0.4633 - ETA: 1s - loss: 8.3092 - acc: 0.4622 - ETA: 1s - loss: 8.2708 - acc: 0.4651 - ETA: 1s - loss: 8.3169 - acc: 0.4630 - ETA: 1s - loss: 8.3077 - acc: 0.4636 - ETA: 1s - loss: 8.3901 - acc: 0.4592 - ETA: 1s - loss: 8.4074 - acc: 0.4583 - ETA: 1s - loss: 8.4898 - acc: 0.4531 - ETA: 1s - loss: 8.4890 - acc: 0.4526 - ETA: 1s - loss: 8.4678 - acc: 0.4535 - ETA: 1s - loss: 8.4988 - acc: 0.4513 - ETA: 1s - loss: 8.4737 - acc: 0.4529 - ETA: 1s - loss: 8.5017 - acc: 0.4512 - ETA: 1s - loss: 8.4779 - acc: 0.4520 - ETA: 1s - loss: 8.4624 - acc: 0.4528 - ETA: 1s - loss: 8.4487 - acc: 0.4530 - ETA: 1s - loss: 8.4841 - acc: 0.4505 - ETA: 0s - loss: 8.4809 - acc: 0.4498 - ETA: 0s - loss: 8.4814 - acc: 0.4502 - ETA: 0s - loss: 8.4697 - acc: 0.4505 - ETA: 0s - loss: 8.4581 - acc: 0.4500 - ETA: 0s - loss: 8.4586 - acc: 0.4502 - ETA: 0s - loss: 8.4758 - acc: 0.4491 - ETA: 0s - loss: 8.4687 - acc: 0.4492 - ETA: 0s - loss: 8.4423 - acc: 0.4504 - ETA: 0s - loss: 8.4291 - acc: 0.4510 - ETA: 0s - loss: 8.4306 - acc: 0.4506 - ETA: 0s - loss: 8.4489 - acc: 0.4480 - ETA: 0s - loss: 8.4546 - acc: 0.4471 - ETA: 0s - loss: 8.4826 - acc: 0.4451 - ETA: 0s - loss: 8.4678 - acc: 0.4466 - ETA: 0s - loss: 8.4395 - acc: 0.4479 - ETA: 0s - loss: 8.4532 - acc: 0.4466 - ETA: 0s - loss: 8.4698 - acc: 0.4458 - ETA: 0s - loss: 8.4928 - acc: 0.4442Epoch 00006: val_loss improved from 9.13905 to 8.91883, saving model to weights.best.VGG16.hdf5 6680/6680 [==============================] - 2s - loss: 8.5197 - acc: 0.4422 - val_loss: 8.9188 - val_acc: 0.3593 Epoch 8/20 6580/6680 [============================>.] - ETA: 2s - loss: 5.6706 - acc: 0.6500 - ETA: 2s - loss: 7.6052 - acc: 0.5000 - ETA: 2s - loss: 8.3374 - acc: 0.4633 - ETA: 2s - loss: 8.4189 - acc: 0.4545 - ETA: 2s - loss: 8.3928 - acc: 0.4534 - ETA: 2s - loss: 8.5324 - acc: 0.4431 - ETA: 2s - loss: 8.4775 - acc: 0.4420 - ETA: 2s - loss: 8.5052 - acc: 0.4431 - ETA: 2s - loss: 8.5769 - acc: 0.4404 - ETA: 1s - loss: 8.3954 - acc: 0.4500 - ETA: 1s - loss: 8.2935 - acc: 0.4535 - ETA: 1s - loss: 8.1918 - acc: 0.4588 - ETA: 1s - loss: 8.2733 - acc: 0.4511 - ETA: 1s - loss: 8.2299 - acc: 0.4505 - ETA: 1s - loss: 8.1957 - acc: 0.4520 - ETA: 1s - loss: 8.1905 - acc: 0.4509 - ETA: 1s - loss: 8.2271 - acc: 0.4491 - ETA: 1s - loss: 8.2201 - acc: 0.4508 - ETA: 1s - loss: 8.2439 - acc: 0.4496 - ETA: 1s - loss: 8.1559 - acc: 0.4559 - ETA: 1s - loss: 8.1909 - acc: 0.4531 - ETA: 1s - loss: 8.1200 - acc: 0.4573 - ETA: 1s - loss: 8.1164 - acc: 0.4579 - ETA: 1s - loss: 8.0717 - acc: 0.4599 - ETA: 1s - loss: 8.0769 - acc: 0.4601 - ETA: 1s - loss: 8.0535 - acc: 0.4625 - ETA: 1s - loss: 8.0245 - acc: 0.4644 - ETA: 1s - loss: 8.0376 - acc: 0.4637 - ETA: 0s - loss: 8.0399 - acc: 0.4632 - ETA: 0s - loss: 8.0714 - acc: 0.4615 - ETA: 0s - loss: 8.0951 - acc: 0.4600 - ETA: 0s - loss: 8.1031 - acc: 0.4595 - ETA: 0s - loss: 8.1142 - acc: 0.4580 - ETA: 0s - loss: 8.0760 - acc: 0.4605 - ETA: 0s - loss: 8.0641 - acc: 0.4620 - ETA: 0s - loss: 8.0474 - acc: 0.4625 - ETA: 0s - loss: 8.0433 - acc: 0.4631 - ETA: 0s - loss: 8.0381 - acc: 0.4643 - ETA: 0s - loss: 8.0182 - acc: 0.4659 - ETA: 0s - loss: 8.0396 - acc: 0.4650 - ETA: 0s - loss: 8.0423 - acc: 0.4653 - ETA: 0s - loss: 8.0178 - acc: 0.4665 - ETA: 0s - loss: 8.0351 - acc: 0.4651 - ETA: 0s - loss: 8.0410 - acc: 0.4649 - ETA: 0s - loss: 8.0533 - acc: 0.4644 - ETA: 0s - loss: 8.0628 - acc: 0.4643 - ETA: 0s - loss: 8.0670 - acc: 0.4644Epoch 00007: val_loss improved from 8.91883 to 8.52306, saving model to weights.best.VGG16.hdf5 6680/6680 [==============================] - 2s - loss: 8.0725 - acc: 0.4644 - val_loss: 8.5231 - val_acc: 0.3892 Epoch 9/20 6660/6680 [============================>.] - ETA: 2s - loss: 8.8947 - acc: 0.4500 - ETA: 2s - loss: 8.1002 - acc: 0.4875 - ETA: 2s - loss: 7.6296 - acc: 0.5067 - ETA: 2s - loss: 7.9216 - acc: 0.4886 - ETA: 2s - loss: 7.8033 - acc: 0.4933 - ETA: 2s - loss: 7.9177 - acc: 0.4851 - ETA: 2s - loss: 7.9145 - acc: 0.4852 - ETA: 2s - loss: 7.9843 - acc: 0.4808 - ETA: 2s - loss: 7.9421 - acc: 0.4842 - ETA: 1s - loss: 7.8589 - acc: 0.4903 - ETA: 1s - loss: 7.8521 - acc: 0.4926 - ETA: 1s - loss: 7.8933 - acc: 0.4889 - ETA: 1s - loss: 7.8438 - acc: 0.4920 - ETA: 1s - loss: 7.8491 - acc: 0.4916 - ETA: 1s - loss: 7.8521 - acc: 0.4917 - ETA: 1s - loss: 7.8556 - acc: 0.4913 - ETA: 1s - loss: 7.8904 - acc: 0.4897 - ETA: 1s - loss: 7.8760 - acc: 0.4907 - ETA: 1s - loss: 7.9117 - acc: 0.4892 - ETA: 1s - loss: 7.8685 - acc: 0.4909 - ETA: 1s - loss: 7.8469 - acc: 0.4914 - ETA: 1s - loss: 7.8668 - acc: 0.4905 - ETA: 1s - loss: 7.8922 - acc: 0.4888 - ETA: 1s - loss: 7.9178 - acc: 0.4874 - ETA: 1s - loss: 7.9657 - acc: 0.4834 - ETA: 1s - loss: 7.9325 - acc: 0.4852 - ETA: 1s - loss: 7.9168 - acc: 0.4858 - ETA: 1s - loss: 7.9199 - acc: 0.4858 - ETA: 0s - loss: 7.8874 - acc: 0.4874 - ETA: 0s - loss: 7.8978 - acc: 0.4865 - ETA: 0s - loss: 7.8996 - acc: 0.4865 - ETA: 0s - loss: 7.9285 - acc: 0.4847 - ETA: 0s - loss: 7.9252 - acc: 0.4850 - ETA: 0s - loss: 7.9253 - acc: 0.4850 - ETA: 0s - loss: 7.9451 - acc: 0.4842 - ETA: 0s - loss: 7.9424 - acc: 0.4846 - ETA: 0s - loss: 7.9552 - acc: 0.4833 - ETA: 0s - loss: 7.9486 - acc: 0.4836 - ETA: 0s - loss: 7.9531 - acc: 0.4829 - ETA: 0s - loss: 7.9577 - acc: 0.4823 - ETA: 0s - loss: 7.9554 - acc: 0.4826 - ETA: 0s - loss: 7.9450 - acc: 0.4837 - ETA: 0s - loss: 7.9240 - acc: 0.4846 - ETA: 0s - loss: 7.9082 - acc: 0.4854 - ETA: 0s - loss: 7.9075 - acc: 0.4853 - ETA: 0s - loss: 7.8935 - acc: 0.4859 - ETA: 0s - loss: 7.8823 - acc: 0.4865Epoch 00008: val_loss improved from 8.52306 to 8.44016, saving model to weights.best.VGG16.hdf5 6680/6680 [==============================] - 2s - loss: 7.8839 - acc: 0.4861 - val_loss: 8.4402 - val_acc: 0.4012 Epoch 10/20 6640/6680 [============================>.] - ETA: 2s - loss: 4.8366 - acc: 0.7000 - ETA: 2s - loss: 9.3440 - acc: 0.4062 - ETA: 2s - loss: 8.8731 - acc: 0.4300 - ETA: 2s - loss: 8.3799 - acc: 0.4636 - ETA: 2s - loss: 8.2034 - acc: 0.4741 - ETA: 2s - loss: 8.1923 - acc: 0.4750 - ETA: 2s - loss: 8.0851 - acc: 0.4826 - ETA: 2s - loss: 8.0130 - acc: 0.4860 - ETA: 2s - loss: 7.8975 - acc: 0.4939 - ETA: 2s - loss: 7.8890 - acc: 0.4945 - ETA: 1s - loss: 7.7692 - acc: 0.5021 - ETA: 1s - loss: 7.7745 - acc: 0.5000 - ETA: 1s - loss: 7.7870 - acc: 0.4988 - ETA: 1s - loss: 7.7501 - acc: 0.5022 - ETA: 1s - loss: 7.7508 - acc: 0.5020 - ETA: 1s - loss: 7.7411 - acc: 0.5009 - ETA: 1s - loss: 7.7231 - acc: 0.5026 - ETA: 1s - loss: 7.7195 - acc: 0.5029 - ETA: 1s - loss: 7.7280 - acc: 0.5020 - ETA: 1s - loss: 7.7796 - acc: 0.4993 - ETA: 1s - loss: 7.8117 - acc: 0.4972 - ETA: 1s - loss: 7.8509 - acc: 0.4953 - ETA: 1s - loss: 7.8474 - acc: 0.4955 - ETA: 1s - loss: 7.8658 - acc: 0.4942 - ETA: 1s - loss: 7.8566 - acc: 0.4944 - ETA: 1s - loss: 7.8846 - acc: 0.4921 - ETA: 1s - loss: 7.8349 - acc: 0.4957 - ETA: 1s - loss: 7.8379 - acc: 0.4948 - ETA: 1s - loss: 7.7987 - acc: 0.4970 - ETA: 0s - loss: 7.7772 - acc: 0.4983 - ETA: 0s - loss: 7.7816 - acc: 0.4981 - ETA: 0s - loss: 7.8018 - acc: 0.4973 - ETA: 0s - loss: 7.7890 - acc: 0.4967 - ETA: 0s - loss: 7.8133 - acc: 0.4955 - ETA: 0s - loss: 7.8092 - acc: 0.4954 - ETA: 0s - loss: 7.7773 - acc: 0.4974 - ETA: 0s - loss: 7.7819 - acc: 0.4972 - ETA: 0s - loss: 7.7866 - acc: 0.4971 - ETA: 0s - loss: 7.7804 - acc: 0.4976 - ETA: 0s - loss: 7.7823 - acc: 0.4973 - ETA: 0s - loss: 7.7801 - acc: 0.4973 - ETA: 0s - loss: 7.7849 - acc: 0.4969 - ETA: 0s - loss: 7.7871 - acc: 0.4968 - ETA: 0s - loss: 7.8077 - acc: 0.4954 - ETA: 0s - loss: 7.7969 - acc: 0.4960 - ETA: 0s - loss: 7.7852 - acc: 0.4970 - ETA: 0s - loss: 7.7845 - acc: 0.4974 - ETA: 0s - loss: 7.7643 - acc: 0.4986Epoch 00009: val_loss improved from 8.44016 to 8.38619, saving model to weights.best.VGG16.hdf5 6680/6680 [==============================] - 2s - loss: 7.7542 - acc: 0.4994 - val_loss: 8.3862 - val_acc: 0.4048 Epoch 11/20 6540/6680 [============================>.] - ETA: 2s - loss: 6.4714 - acc: 0.6000 - ETA: 2s - loss: 6.1066 - acc: 0.6125 - ETA: 2s - loss: 7.1041 - acc: 0.5500 - ETA: 2s - loss: 7.1528 - acc: 0.5500 - ETA: 2s - loss: 7.3590 - acc: 0.5345 - ETA: 2s - loss: 7.3652 - acc: 0.5333 - ETA: 2s - loss: 7.4004 - acc: 0.5279 - ETA: 2s - loss: 7.3975 - acc: 0.5260 - ETA: 2s - loss: 7.4288 - acc: 0.5246 - ETA: 2s - loss: 7.5515 - acc: 0.5164 - ETA: 1s - loss: 7.5720 - acc: 0.5155 - ETA: 1s - loss: 7.5235 - acc: 0.5199 - ETA: 1s - loss: 7.5241 - acc: 0.5200 - ETA: 1s - loss: 7.4982 - acc: 0.5217 - ETA: 1s - loss: 7.4836 - acc: 0.5225 - ETA: 1s - loss: 7.5526 - acc: 0.5176 - ETA: 1s - loss: 7.5814 - acc: 0.5139 - ETA: 1s - loss: 7.6796 - acc: 0.5074 - ETA: 1s - loss: 7.6992 - acc: 0.5062 - ETA: 1s - loss: 7.7180 - acc: 0.5055 - ETA: 1s - loss: 7.7095 - acc: 0.5059 - ETA: 1s - loss: 7.6481 - acc: 0.5090 - ETA: 1s - loss: 7.5668 - acc: 0.5140 - ETA: 1s - loss: 7.5268 - acc: 0.5168 - ETA: 1s - loss: 7.5497 - acc: 0.5158 - ETA: 1s - loss: 7.5923 - acc: 0.5132 - ETA: 1s - loss: 7.6051 - acc: 0.5122 - ETA: 1s - loss: 7.6239 - acc: 0.5109 - ETA: 0s - loss: 7.5976 - acc: 0.5126 - ETA: 0s - loss: 7.5830 - acc: 0.5133 - ETA: 0s - loss: 7.5731 - acc: 0.5140 - ETA: 0s - loss: 7.5419 - acc: 0.5154 - ETA: 0s - loss: 7.5568 - acc: 0.5140 - ETA: 0s - loss: 7.5611 - acc: 0.5132 - ETA: 0s - loss: 7.5687 - acc: 0.5122 - ETA: 0s - loss: 7.5939 - acc: 0.5100 - ETA: 0s - loss: 7.5919 - acc: 0.5102 - ETA: 0s - loss: 7.6252 - acc: 0.5082 - ETA: 0s - loss: 7.6345 - acc: 0.5078 - ETA: 0s - loss: 7.6395 - acc: 0.5076 - ETA: 0s - loss: 7.6582 - acc: 0.5063 - ETA: 0s - loss: 7.6386 - acc: 0.5076 - ETA: 0s - loss: 7.6450 - acc: 0.5062 - ETA: 0s - loss: 7.6411 - acc: 0.5062 - ETA: 0s - loss: 7.6282 - acc: 0.5069 - ETA: 0s - loss: 7.6084 - acc: 0.5083 - ETA: 0s - loss: 7.6162 - acc: 0.5076Epoch 00010: val_loss improved from 8.38619 to 8.12543, saving model to weights.best.VGG16.hdf5 6680/6680 [==============================] - 2s - loss: 7.6290 - acc: 0.5069 - val_loss: 8.1254 - val_acc: 0.4204 Epoch 12/20 6540/6680 [============================>.] - ETA: 2s - loss: 8.0595 - acc: 0.5000 - ETA: 2s - loss: 7.3829 - acc: 0.5278 - ETA: 2s - loss: 7.4089 - acc: 0.5281 - ETA: 2s - loss: 7.4770 - acc: 0.5196 - ETA: 2s - loss: 7.4631 - acc: 0.5183 - ETA: 2s - loss: 7.4256 - acc: 0.5230 - ETA: 2s - loss: 7.5039 - acc: 0.5182 - ETA: 2s - loss: 7.4778 - acc: 0.5176 - ETA: 2s - loss: 7.3535 - acc: 0.5259 - ETA: 1s - loss: 7.4328 - acc: 0.5215 - ETA: 1s - loss: 7.4494 - acc: 0.5222 - ETA: 1s - loss: 7.4447 - acc: 0.5209 - ETA: 1s - loss: 7.3822 - acc: 0.5238 - ETA: 1s - loss: 7.3914 - acc: 0.5237 - ETA: 1s - loss: 7.4539 - acc: 0.5205 - ETA: 1s - loss: 7.4851 - acc: 0.5182 - ETA: 1s - loss: 7.5330 - acc: 0.5158 - ETA: 1s - loss: 7.5291 - acc: 0.5165 - ETA: 1s - loss: 7.4821 - acc: 0.5191 - ETA: 1s - loss: 7.4771 - acc: 0.5193 - ETA: 1s - loss: 7.4817 - acc: 0.5187 - ETA: 1s - loss: 7.4837 - acc: 0.5178 - ETA: 1s - loss: 7.4801 - acc: 0.5183 - ETA: 1s - loss: 7.5192 - acc: 0.5160 - ETA: 1s - loss: 7.4922 - acc: 0.5176 - ETA: 1s - loss: 7.5217 - acc: 0.5164 - ETA: 1s - loss: 7.5274 - acc: 0.5163 - ETA: 1s - loss: 7.5488 - acc: 0.5147 - ETA: 1s - loss: 7.5243 - acc: 0.5162 - ETA: 0s - loss: 7.4984 - acc: 0.5178 - ETA: 0s - loss: 7.4587 - acc: 0.5196 - ETA: 0s - loss: 7.4476 - acc: 0.5202 - ETA: 0s - loss: 7.4542 - acc: 0.5198 - ETA: 0s - loss: 7.4759 - acc: 0.5186 - ETA: 0s - loss: 7.4504 - acc: 0.5202 - ETA: 0s - loss: 7.4631 - acc: 0.5189 - ETA: 0s - loss: 7.4866 - acc: 0.5176 - ETA: 0s - loss: 7.4690 - acc: 0.5184 - ETA: 0s - loss: 7.4702 - acc: 0.5186 - ETA: 0s - loss: 7.4917 - acc: 0.5174 - ETA: 0s - loss: 7.4713 - acc: 0.5188 - ETA: 0s - loss: 7.4936 - acc: 0.5176 - ETA: 0s - loss: 7.4758 - acc: 0.5179 - ETA: 0s - loss: 7.4896 - acc: 0.5173 - ETA: 0s - loss: 7.4967 - acc: 0.5168 - ETA: 0s - loss: 7.4893 - acc: 0.5175 - ETA: 0s - loss: 7.4916 - acc: 0.5176Epoch 00011: val_loss did not improve 6680/6680 [==============================] - 2s - loss: 7.4776 - acc: 0.5183 - val_loss: 8.1265 - val_acc: 0.4156 Epoch 13/20 6540/6680 [============================>.] - ETA: 2s - loss: 7.3014 - acc: 0.5000 - ETA: 2s - loss: 7.1076 - acc: 0.5375 - ETA: 2s - loss: 7.3450 - acc: 0.5300 - ETA: 2s - loss: 7.4362 - acc: 0.5250 - ETA: 2s - loss: 7.3710 - acc: 0.5293 - ETA: 2s - loss: 7.4653 - acc: 0.5250 - ETA: 2s - loss: 7.4129 - acc: 0.5302 - ETA: 2s - loss: 7.4879 - acc: 0.5270 - ETA: 2s - loss: 7.4352 - acc: 0.5298 - ETA: 2s - loss: 7.5710 - acc: 0.5211 - ETA: 1s - loss: 7.4744 - acc: 0.5257 - ETA: 1s - loss: 7.4557 - acc: 0.5278 - ETA: 1s - loss: 7.4790 - acc: 0.5267 - ETA: 1s - loss: 7.4142 - acc: 0.5290 - ETA: 1s - loss: 7.3952 - acc: 0.5310 - ETA: 1s - loss: 7.3384 - acc: 0.5346 - ETA: 1s - loss: 7.3210 - acc: 0.5355 - ETA: 1s - loss: 7.3129 - acc: 0.5360 - ETA: 1s - loss: 7.3262 - acc: 0.5352 - ETA: 1s - loss: 7.3675 - acc: 0.5319 - ETA: 1s - loss: 7.3732 - acc: 0.5320 - ETA: 1s - loss: 7.3894 - acc: 0.5315 - ETA: 1s - loss: 7.3920 - acc: 0.5315 - ETA: 1s - loss: 7.4032 - acc: 0.5306 - ETA: 1s - loss: 7.3650 - acc: 0.5332 - ETA: 1s - loss: 7.3765 - acc: 0.5328 - ETA: 1s - loss: 7.3652 - acc: 0.5329 - ETA: 1s - loss: 7.3491 - acc: 0.5332 - ETA: 0s - loss: 7.3564 - acc: 0.5331 - ETA: 0s - loss: 7.3628 - acc: 0.5322 - ETA: 0s - loss: 7.3870 - acc: 0.5305 - ETA: 0s - loss: 7.3709 - acc: 0.5311 - ETA: 0s - loss: 7.3571 - acc: 0.5323 - ETA: 0s - loss: 7.3788 - acc: 0.5309 - ETA: 0s - loss: 7.3895 - acc: 0.5302 - ETA: 0s - loss: 7.4006 - acc: 0.5296 - ETA: 0s - loss: 7.4038 - acc: 0.5290 - ETA: 0s - loss: 7.3858 - acc: 0.5303 - ETA: 0s - loss: 7.3624 - acc: 0.5319 - ETA: 0s - loss: 7.3859 - acc: 0.5306 - ETA: 0s - loss: 7.3850 - acc: 0.5302 - ETA: 0s - loss: 7.3848 - acc: 0.5300 - ETA: 0s - loss: 7.3854 - acc: 0.5299 - ETA: 0s - loss: 7.3788 - acc: 0.5302 - ETA: 0s - loss: 7.3892 - acc: 0.5297 - ETA: 0s - loss: 7.3983 - acc: 0.5288 - ETA: 0s - loss: 7.4132 - acc: 0.5277Epoch 00012: val_loss improved from 8.12543 to 8.08215, saving model to weights.best.VGG16.hdf5 6680/6680 [==============================] - 2s - loss: 7.4336 - acc: 0.5263 - val_loss: 8.0821 - val_acc: 0.4240 Epoch 14/20 6620/6680 [============================>.] - ETA: 2s - loss: 8.0941 - acc: 0.5000 - ETA: 2s - loss: 7.6756 - acc: 0.5125 - ETA: 2s - loss: 7.4931 - acc: 0.5267 - ETA: 2s - loss: 7.6002 - acc: 0.5227 - ETA: 2s - loss: 7.6592 - acc: 0.5190 - ETA: 2s - loss: 7.6436 - acc: 0.5208 - ETA: 2s - loss: 7.5260 - acc: 0.5267 - ETA: 2s - loss: 7.4002 - acc: 0.5330 - ETA: 2s - loss: 7.4082 - acc: 0.5330 - ETA: 2s - loss: 7.2301 - acc: 0.5445 - ETA: 1s - loss: 7.3640 - acc: 0.5352 - ETA: 1s - loss: 7.3927 - acc: 0.5321 - ETA: 1s - loss: 7.3340 - acc: 0.5365 - ETA: 1s - loss: 7.2730 - acc: 0.5402 - ETA: 1s - loss: 7.3296 - acc: 0.5364 - ETA: 1s - loss: 7.3579 - acc: 0.5344 - ETA: 1s - loss: 7.4096 - acc: 0.5314 - ETA: 1s - loss: 7.4141 - acc: 0.5313 - ETA: 1s - loss: 7.4443 - acc: 0.5295 - ETA: 1s - loss: 7.4426 - acc: 0.5291 - ETA: 1s - loss: 7.4442 - acc: 0.5280 - ETA: 1s - loss: 7.4643 - acc: 0.5260 - ETA: 1s - loss: 7.4886 - acc: 0.5242 - ETA: 1s - loss: 7.4677 - acc: 0.5244 - ETA: 1s - loss: 7.4641 - acc: 0.5251 - ETA: 1s - loss: 7.4570 - acc: 0.5256 - ETA: 1s - loss: 7.4051 - acc: 0.5284 - ETA: 1s - loss: 7.3886 - acc: 0.5292 - ETA: 1s - loss: 7.3955 - acc: 0.5277 - ETA: 0s - loss: 7.4001 - acc: 0.5275 - ETA: 0s - loss: 7.4157 - acc: 0.5265 - ETA: 0s - loss: 7.4603 - acc: 0.5236 - ETA: 0s - loss: 7.4737 - acc: 0.5224 - ETA: 0s - loss: 7.4221 - acc: 0.5254 - ETA: 0s - loss: 7.4006 - acc: 0.5266 - ETA: 0s - loss: 7.3817 - acc: 0.5278 - ETA: 0s - loss: 7.3885 - acc: 0.5271 - ETA: 0s - loss: 7.3730 - acc: 0.5277 - ETA: 0s - loss: 7.3504 - acc: 0.5292 - ETA: 0s - loss: 7.3743 - acc: 0.5276 - ETA: 0s - loss: 7.3803 - acc: 0.5272 - ETA: 0s - loss: 7.3809 - acc: 0.5267 - ETA: 0s - loss: 7.3794 - acc: 0.5264 - ETA: 0s - loss: 7.3609 - acc: 0.5276 - ETA: 0s - loss: 7.3669 - acc: 0.5273 - ETA: 0s - loss: 7.3631 - acc: 0.5275 - ETA: 0s - loss: 7.3631 - acc: 0.5276 - ETA: 0s - loss: 7.3676 - acc: 0.5273Epoch 00013: val_loss improved from 8.08215 to 7.92109, saving model to weights.best.VGG16.hdf5 6680/6680 [==============================] - 2s - loss: 7.3757 - acc: 0.5268 - val_loss: 7.9211 - val_acc: 0.4335 Epoch 15/20 6660/6680 [============================>.] - ETA: 2s - loss: 8.0597 - acc: 0.5000 - ETA: 2s - loss: 7.0553 - acc: 0.5625 - ETA: 2s - loss: 7.1061 - acc: 0.5500 - ETA: 2s - loss: 7.0452 - acc: 0.5543 - ETA: 2s - loss: 6.9602 - acc: 0.5600 - ETA: 2s - loss: 7.2776 - acc: 0.5419 - ETA: 2s - loss: 7.2523 - acc: 0.5443 - ETA: 2s - loss: 7.1849 - acc: 0.5461 - ETA: 1s - loss: 7.1890 - acc: 0.5441 - ETA: 1s - loss: 7.2405 - acc: 0.5379 - ETA: 1s - loss: 7.2221 - acc: 0.5390 - ETA: 1s - loss: 7.1949 - acc: 0.5413 - ETA: 1s - loss: 7.2003 - acc: 0.5420 - ETA: 1s - loss: 7.1085 - acc: 0.5458 - ETA: 1s - loss: 7.1930 - acc: 0.5412 - ETA: 1s - loss: 7.1565 - acc: 0.5431 - ETA: 1s - loss: 7.1193 - acc: 0.5453 - ETA: 1s - loss: 7.1751 - acc: 0.5423 - ETA: 1s - loss: 7.2107 - acc: 0.5408 - ETA: 1s - loss: 7.1719 - acc: 0.5438 - ETA: 1s - loss: 7.2445 - acc: 0.5396 - ETA: 1s - loss: 7.2175 - acc: 0.5411 - ETA: 1s - loss: 7.1996 - acc: 0.5424 - ETA: 1s - loss: 7.1920 - acc: 0.5427 - ETA: 1s - loss: 7.2182 - acc: 0.5401 - ETA: 1s - loss: 7.2020 - acc: 0.5402 - ETA: 1s - loss: 7.1927 - acc: 0.5395 - ETA: 1s - loss: 7.1933 - acc: 0.5391 - ETA: 0s - loss: 7.2114 - acc: 0.5383 - ETA: 0s - loss: 7.2105 - acc: 0.5384 - ETA: 0s - loss: 7.1625 - acc: 0.5411 - ETA: 0s - loss: 7.1851 - acc: 0.5398 - ETA: 0s - loss: 7.2071 - acc: 0.5386 - ETA: 0s - loss: 7.2043 - acc: 0.5383 - ETA: 0s - loss: 7.2029 - acc: 0.5384 - ETA: 0s - loss: 7.1919 - acc: 0.5392 - ETA: 0s - loss: 7.1842 - acc: 0.5391 - ETA: 0s - loss: 7.2045 - acc: 0.5376 - ETA: 0s - loss: 7.1925 - acc: 0.5387 - ETA: 0s - loss: 7.2069 - acc: 0.5381 - ETA: 0s - loss: 7.2069 - acc: 0.5377 - ETA: 0s - loss: 7.2085 - acc: 0.5376 - ETA: 0s - loss: 7.2135 - acc: 0.5372 - ETA: 0s - loss: 7.2098 - acc: 0.5375 - ETA: 0s - loss: 7.1981 - acc: 0.5381 - ETA: 0s - loss: 7.1909 - acc: 0.5386 - ETA: 0s - loss: 7.1996 - acc: 0.5382 - ETA: 0s - loss: 7.1673 - acc: 0.5404Epoch 00014: val_loss improved from 7.92109 to 7.86037, saving model to weights.best.VGG16.hdf5 6680/6680 [==============================] - 2s - loss: 7.1618 - acc: 0.5407 - val_loss: 7.8604 - val_acc: 0.4491 Epoch 16/20 6620/6680 [============================>.] - ETA: 2s - loss: 8.9890 - acc: 0.4000 - ETA: 2s - loss: 7.2796 - acc: 0.5375 - ETA: 2s - loss: 7.2819 - acc: 0.5367 - ETA: 2s - loss: 7.3100 - acc: 0.5386 - ETA: 2s - loss: 7.1269 - acc: 0.5500 - ETA: 2s - loss: 7.0027 - acc: 0.5554 - ETA: 2s - loss: 7.0909 - acc: 0.5489 - ETA: 2s - loss: 7.1438 - acc: 0.5461 - ETA: 2s - loss: 7.1904 - acc: 0.5440 - ETA: 1s - loss: 7.2594 - acc: 0.5408 - ETA: 1s - loss: 7.2044 - acc: 0.5444 - ETA: 1s - loss: 7.1826 - acc: 0.5449 - ETA: 1s - loss: 7.2076 - acc: 0.5442 - ETA: 1s - loss: 7.2760 - acc: 0.5403 - ETA: 1s - loss: 7.1922 - acc: 0.5455 - ETA: 1s - loss: 7.1238 - acc: 0.5495 - ETA: 1s - loss: 7.0808 - acc: 0.5522 - ETA: 1s - loss: 7.0969 - acc: 0.5517 - ETA: 1s - loss: 7.0587 - acc: 0.5535 - ETA: 1s - loss: 7.0580 - acc: 0.5537 - ETA: 1s - loss: 7.0490 - acc: 0.5535 - ETA: 1s - loss: 6.9974 - acc: 0.5567 - ETA: 1s - loss: 7.0001 - acc: 0.5564 - ETA: 1s - loss: 7.0574 - acc: 0.5521 - ETA: 1s - loss: 7.0902 - acc: 0.5503 - ETA: 1s - loss: 7.0694 - acc: 0.5520 - ETA: 1s - loss: 7.0713 - acc: 0.5514 - ETA: 1s - loss: 7.0828 - acc: 0.5508 - ETA: 1s - loss: 7.0630 - acc: 0.5518 - ETA: 0s - loss: 7.0871 - acc: 0.5500 - ETA: 0s - loss: 7.1038 - acc: 0.5483 - ETA: 0s - loss: 7.1018 - acc: 0.5486 - ETA: 0s - loss: 7.1162 - acc: 0.5478 - ETA: 0s - loss: 7.1450 - acc: 0.5461 - ETA: 0s - loss: 7.1488 - acc: 0.5460 - ETA: 0s - loss: 7.1076 - acc: 0.5480 - ETA: 0s - loss: 7.0740 - acc: 0.5504 - ETA: 0s - loss: 7.1019 - acc: 0.5489 - ETA: 0s - loss: 7.1079 - acc: 0.5483 - ETA: 0s - loss: 7.1320 - acc: 0.5469 - ETA: 0s - loss: 7.1385 - acc: 0.5466 - ETA: 0s - loss: 7.1242 - acc: 0.5477 - ETA: 0s - loss: 7.1106 - acc: 0.5485 - ETA: 0s - loss: 7.1031 - acc: 0.5490 - ETA: 0s - loss: 7.0923 - acc: 0.5498 - ETA: 0s - loss: 7.0965 - acc: 0.5497 - ETA: 0s - loss: 7.1113 - acc: 0.5486 - ETA: 0s - loss: 7.0907 - acc: 0.5498Epoch 00015: val_loss improved from 7.86037 to 7.79918, saving model to weights.best.VGG16.hdf5 6680/6680 [==============================] - 2s - loss: 7.0875 - acc: 0.5501 - val_loss: 7.7992 - val_acc: 0.4455 Epoch 17/20 6580/6680 [============================>.] - ETA: 2s - loss: 5.6416 - acc: 0.6500 - ETA: 2s - loss: 6.7193 - acc: 0.5833 - ETA: 2s - loss: 6.8264 - acc: 0.5625 - ETA: 2s - loss: 6.8208 - acc: 0.5652 - ETA: 2s - loss: 6.7622 - acc: 0.5717 - ETA: 2s - loss: 6.8088 - acc: 0.5689 - ETA: 2s - loss: 6.9427 - acc: 0.5602 - ETA: 2s - loss: 7.0682 - acc: 0.5529 - ETA: 2s - loss: 7.0695 - acc: 0.5517 - ETA: 1s - loss: 7.2387 - acc: 0.5415 - ETA: 1s - loss: 7.2751 - acc: 0.5396 - ETA: 1s - loss: 7.1830 - acc: 0.5456 - ETA: 1s - loss: 7.2052 - acc: 0.5435 - ETA: 1s - loss: 7.2452 - acc: 0.5413 - ETA: 1s - loss: 7.2063 - acc: 0.5439 - ETA: 1s - loss: 7.1830 - acc: 0.5448 - ETA: 1s - loss: 7.1467 - acc: 0.5469 - ETA: 1s - loss: 7.1736 - acc: 0.5458 - ETA: 1s - loss: 7.1849 - acc: 0.5457 - ETA: 1s - loss: 7.2083 - acc: 0.5440 - ETA: 1s - loss: 7.1934 - acc: 0.5454 - ETA: 1s - loss: 7.1726 - acc: 0.5463 - ETA: 1s - loss: 7.2394 - acc: 0.5423 - ETA: 1s - loss: 7.2118 - acc: 0.5441 - ETA: 1s - loss: 7.2136 - acc: 0.5444 - ETA: 1s - loss: 7.1924 - acc: 0.5460 - ETA: 1s - loss: 7.1522 - acc: 0.5481 - ETA: 1s - loss: 7.1564 - acc: 0.5479 - ETA: 1s - loss: 7.1137 - acc: 0.5503 - ETA: 0s - loss: 7.1304 - acc: 0.5495 - ETA: 0s - loss: 7.1159 - acc: 0.5505 - ETA: 0s - loss: 7.0815 - acc: 0.5525 - ETA: 0s - loss: 7.0925 - acc: 0.5513 - ETA: 0s - loss: 7.0824 - acc: 0.5515 - ETA: 0s - loss: 7.1372 - acc: 0.5479 - ETA: 0s - loss: 7.1196 - acc: 0.5488 - ETA: 0s - loss: 7.1208 - acc: 0.5486 - ETA: 0s - loss: 7.1175 - acc: 0.5488 - ETA: 0s - loss: 7.1453 - acc: 0.5474 - ETA: 0s - loss: 7.1208 - acc: 0.5487 - ETA: 0s - loss: 7.1159 - acc: 0.5491 - ETA: 0s - loss: 7.1042 - acc: 0.5495 - ETA: 0s - loss: 7.1119 - acc: 0.5488 - ETA: 0s - loss: 7.1102 - acc: 0.5492 - ETA: 0s - loss: 7.1185 - acc: 0.5487 - ETA: 0s - loss: 7.0934 - acc: 0.5505 - ETA: 0s - loss: 7.0771 - acc: 0.5516 - ETA: 0s - loss: 7.0639 - acc: 0.5526Epoch 00016: val_loss improved from 7.79918 to 7.73562, saving model to weights.best.VGG16.hdf5 6680/6680 [==============================] - 2s - loss: 7.0624 - acc: 0.5525 - val_loss: 7.7356 - val_acc: 0.4479 Epoch 18/20 6620/6680 [============================>.] - ETA: 2s - loss: 8.8650 - acc: 0.4500 - ETA: 2s - loss: 6.8963 - acc: 0.5722 - ETA: 2s - loss: 6.9022 - acc: 0.5719 - ETA: 2s - loss: 6.7415 - acc: 0.5783 - ETA: 2s - loss: 6.7443 - acc: 0.5790 - ETA: 2s - loss: 6.6052 - acc: 0.5882 - ETA: 2s - loss: 6.6246 - acc: 0.5867 - ETA: 2s - loss: 6.6348 - acc: 0.5856 - ETA: 1s - loss: 6.6682 - acc: 0.5831 - ETA: 1s - loss: 6.7033 - acc: 0.5803 - ETA: 1s - loss: 6.7674 - acc: 0.5767 - ETA: 1s - loss: 6.7800 - acc: 0.5762 - ETA: 1s - loss: 6.7663 - acc: 0.5747 - ETA: 1s - loss: 6.7769 - acc: 0.5745 - ETA: 1s - loss: 6.8022 - acc: 0.5733 - ETA: 1s - loss: 6.8566 - acc: 0.5699 - ETA: 1s - loss: 6.9037 - acc: 0.5665 - ETA: 1s - loss: 6.8593 - acc: 0.5689 - ETA: 1s - loss: 6.8467 - acc: 0.5698 - ETA: 1s - loss: 6.8562 - acc: 0.5688 - ETA: 1s - loss: 6.8375 - acc: 0.5696 - ETA: 1s - loss: 6.8539 - acc: 0.5683 - ETA: 1s - loss: 6.8358 - acc: 0.5697 - ETA: 1s - loss: 6.8882 - acc: 0.5668 - ETA: 1s - loss: 6.9224 - acc: 0.5646 - ETA: 1s - loss: 6.9278 - acc: 0.5640 - ETA: 1s - loss: 6.9455 - acc: 0.5627 - ETA: 1s - loss: 6.9532 - acc: 0.5622 - ETA: 1s - loss: 6.9886 - acc: 0.5598 - ETA: 0s - loss: 6.9515 - acc: 0.5621 - ETA: 0s - loss: 6.9693 - acc: 0.5610 - ETA: 0s - loss: 6.9418 - acc: 0.5630 - ETA: 0s - loss: 6.9484 - acc: 0.5626 - ETA: 0s - loss: 6.9399 - acc: 0.5631 - ETA: 0s - loss: 6.9360 - acc: 0.5633 - ETA: 0s - loss: 6.9485 - acc: 0.5626 - ETA: 0s - loss: 6.9704 - acc: 0.5612 - ETA: 0s - loss: 7.0312 - acc: 0.5573 - ETA: 0s - loss: 7.0556 - acc: 0.5556 - ETA: 0s - loss: 7.0464 - acc: 0.5560 - ETA: 0s - loss: 7.0202 - acc: 0.5578 - ETA: 0s - loss: 7.0299 - acc: 0.5569 - ETA: 0s - loss: 7.0299 - acc: 0.5571 - ETA: 0s - loss: 7.0300 - acc: 0.5569 - ETA: 0s - loss: 7.0455 - acc: 0.5561 - ETA: 0s - loss: 7.0408 - acc: 0.5563 - ETA: 0s - loss: 7.0350 - acc: 0.5566 - ETA: 0s - loss: 7.0405 - acc: 0.5562Epoch 00017: val_loss did not improve 6680/6680 [==============================] - 2s - loss: 7.0430 - acc: 0.5560 - val_loss: 7.7391 - val_acc: 0.4563 Epoch 19/20 6540/6680 [============================>.] - ETA: 2s - loss: 7.2538 - acc: 0.5500 - ETA: 2s - loss: 6.9822 - acc: 0.5625 - ETA: 2s - loss: 6.9478 - acc: 0.5667 - ETA: 2s - loss: 6.8566 - acc: 0.5727 - ETA: 2s - loss: 7.0885 - acc: 0.5569 - ETA: 2s - loss: 7.1778 - acc: 0.5514 - ETA: 2s - loss: 7.1347 - acc: 0.5547 - ETA: 2s - loss: 7.0763 - acc: 0.5540 - ETA: 2s - loss: 6.8220 - acc: 0.5693 - ETA: 2s - loss: 6.9248 - acc: 0.5633 - ETA: 1s - loss: 7.0107 - acc: 0.5570 - ETA: 1s - loss: 7.2195 - acc: 0.5449 - ETA: 1s - loss: 7.2141 - acc: 0.5453 - ETA: 1s - loss: 7.2398 - acc: 0.5436 - ETA: 1s - loss: 7.1980 - acc: 0.5461 - ETA: 1s - loss: 7.1207 - acc: 0.5509 - ETA: 1s - loss: 7.1219 - acc: 0.5513 - ETA: 1s - loss: 7.1581 - acc: 0.5492 - ETA: 1s - loss: 7.1786 - acc: 0.5477 - ETA: 1s - loss: 7.1196 - acc: 0.5511 - ETA: 1s - loss: 7.1326 - acc: 0.5503 - ETA: 1s - loss: 7.1397 - acc: 0.5497 - ETA: 1s - loss: 7.1498 - acc: 0.5487 - ETA: 1s - loss: 7.1267 - acc: 0.5500 - ETA: 1s - loss: 7.0894 - acc: 0.5523 - ETA: 1s - loss: 7.0300 - acc: 0.5559 - ETA: 1s - loss: 7.0436 - acc: 0.5548 - ETA: 1s - loss: 7.0128 - acc: 0.5570 - ETA: 0s - loss: 6.9619 - acc: 0.5602 - ETA: 0s - loss: 6.9530 - acc: 0.5608 - ETA: 0s - loss: 6.9170 - acc: 0.5626 - ETA: 0s - loss: 6.9172 - acc: 0.5626 - ETA: 0s - loss: 6.9218 - acc: 0.5622 - ETA: 0s - loss: 6.9624 - acc: 0.5600 - ETA: 0s - loss: 6.9589 - acc: 0.5601 - ETA: 0s - loss: 6.9788 - acc: 0.5588 - ETA: 0s - loss: 6.9579 - acc: 0.5603 - ETA: 0s - loss: 6.9547 - acc: 0.5602 - ETA: 0s - loss: 6.9658 - acc: 0.5596 - ETA: 0s - loss: 6.9676 - acc: 0.5595 - ETA: 0s - loss: 6.9608 - acc: 0.5600 - ETA: 0s - loss: 6.9961 - acc: 0.5577 - ETA: 0s - loss: 6.9824 - acc: 0.5584 - ETA: 0s - loss: 6.9629 - acc: 0.5595 - ETA: 0s - loss: 6.9698 - acc: 0.5591 - ETA: 0s - loss: 6.9587 - acc: 0.5598 - ETA: 0s - loss: 6.9771 - acc: 0.5587Epoch 00018: val_loss improved from 7.73562 to 7.67761, saving model to weights.best.VGG16.hdf5 6680/6680 [==============================] - 2s - loss: 6.9754 - acc: 0.5590 - val_loss: 7.6776 - val_acc: 0.4587 Epoch 20/20 6620/6680 [============================>.] - ETA: 2s - loss: 5.5032 - acc: 0.6000 - ETA: 2s - loss: 5.9272 - acc: 0.6250 - ETA: 2s - loss: 6.5486 - acc: 0.5900 - ETA: 2s - loss: 6.6269 - acc: 0.5864 - ETA: 2s - loss: 6.9674 - acc: 0.5655 - ETA: 2s - loss: 7.0039 - acc: 0.5625 - ETA: 2s - loss: 7.1024 - acc: 0.5558 - ETA: 2s - loss: 7.1625 - acc: 0.5510 - ETA: 2s - loss: 7.2173 - acc: 0.5474 - ETA: 2s - loss: 6.9910 - acc: 0.5617 - ETA: 1s - loss: 6.9273 - acc: 0.5655 - ETA: 1s - loss: 6.9786 - acc: 0.5622 - ETA: 1s - loss: 6.9970 - acc: 0.5606 - ETA: 1s - loss: 7.0205 - acc: 0.5582 - ETA: 1s - loss: 7.0621 - acc: 0.5556 - ETA: 1s - loss: 7.0718 - acc: 0.5542 - ETA: 1s - loss: 7.1273 - acc: 0.5509 - ETA: 1s - loss: 7.1185 - acc: 0.5517 - ETA: 1s - loss: 7.0317 - acc: 0.5547 - ETA: 1s - loss: 7.0076 - acc: 0.5563 - ETA: 1s - loss: 7.0121 - acc: 0.5564 - ETA: 1s - loss: 7.0326 - acc: 0.5544 - ETA: 1s - loss: 7.0085 - acc: 0.5548 - ETA: 1s - loss: 6.9807 - acc: 0.5568 - ETA: 1s - loss: 7.0123 - acc: 0.5547 - ETA: 1s - loss: 6.9837 - acc: 0.5560 - ETA: 1s - loss: 6.9764 - acc: 0.5568 - ETA: 1s - loss: 6.9670 - acc: 0.5576 - ETA: 1s - loss: 6.9262 - acc: 0.5596 - ETA: 0s - loss: 6.9261 - acc: 0.5598 - ETA: 0s - loss: 6.8917 - acc: 0.5618 - ETA: 0s - loss: 6.9108 - acc: 0.5610 - ETA: 0s - loss: 6.8626 - acc: 0.5642 - ETA: 0s - loss: 6.8574 - acc: 0.5646 - ETA: 0s - loss: 6.8556 - acc: 0.5650 - ETA: 0s - loss: 6.8581 - acc: 0.5650 - ETA: 0s - loss: 6.8378 - acc: 0.5661 - ETA: 0s - loss: 6.8864 - acc: 0.5632 - ETA: 0s - loss: 6.8694 - acc: 0.5644 - ETA: 0s - loss: 6.8792 - acc: 0.5640 - ETA: 0s - loss: 6.8743 - acc: 0.5645 - ETA: 0s - loss: 6.8813 - acc: 0.5642 - ETA: 0s - loss: 6.8849 - acc: 0.5640 - ETA: 0s - loss: 6.8622 - acc: 0.5655 - ETA: 0s - loss: 6.8586 - acc: 0.5658 - ETA: 0s - loss: 6.8605 - acc: 0.5658 - ETA: 0s - loss: 6.8771 - acc: 0.5648 - ETA: 0s - loss: 6.8594 - acc: 0.5660Epoch 00019: val_loss did not improve 6680/6680 [==============================] - 2s - loss: 6.8536 - acc: 0.5663 - val_loss: 7.6891 - val_acc: 0.4479 ---I am done saving model VGG16----
VGG16_model.load_weights('weights.best.VGG16.hdf5')
Now, we can use the CNN to test how well it identifies breed within our test dataset of dog images. We print the test accuracy below.
# get index of predicted dog breed for each image in test set
VGG16_predictions = [np.argmax(VGG16_model.predict(np.expand_dims(feature, axis=0))) for feature in test_VGG16]
# report test accuracy
test_accuracy = 100*np.sum(np.array(VGG16_predictions)==np.argmax(test_targets, axis=1))/len(VGG16_predictions)
print('Test accuracy: %.4f%%' % test_accuracy)
Test accuracy: 45.8134%
from extract_bottleneck_features import *
def VGG16_predict_breed(img_path):
# extract bottleneck features
bottleneck_feature = extract_VGG16(path_to_tensor(img_path))
# obtain predicted vector
predicted_vector = VGG16_model.predict(bottleneck_feature)
# return dog breed that is predicted by the model
return dog_names[np.argmax(predicted_vector)]
You will now use transfer learning to create a CNN that can identify dog breed from images. Your CNN must attain at least 60% accuracy on the test set.
In Step 4, we used transfer learning to create a CNN using VGG-16 bottleneck features. In this section, you must use the bottleneck features from a different pre-trained model. To make things easier for you, we have pre-computed the features for all of the networks that are currently available in Keras:
The files are encoded as such:
Dog{network}Data.npz
where {network}, in the above filename, can be one of VGG19, Resnet50, InceptionV3, or Xception. Pick one of the above architectures, download the corresponding bottleneck features, and store the downloaded file in the bottleneck_features/ folder in the repository.
In the code block below, extract the bottleneck features corresponding to the train, test, and validation sets by running the following:
bottleneck_features = np.load('bottleneck_features/Dog{network}Data.npz')
train_{network} = bottleneck_features['train']
valid_{network} = bottleneck_features['valid']
test_{network} = bottleneck_features['test']
### TODO: Obtain bottleneck features from another pre-trained CNN.
bottleneck_features_VGG19 = np.load('C:/Training/udacity/AI_NanoDegree/Term2/3.Convolutional Neural Networks Videos/datasets/DogVGG19Data.npz')
train_VGG19 = bottleneck_features_VGG19['train']
valid_VGG19 = bottleneck_features_VGG19['valid']
test_VGG19 = bottleneck_features_VGG19['test']
print('-- Obtained -- VGG19 Bottlenect ---')
bottleneck_features_Resnet50 = np.load('C:/Training/udacity/AI_NanoDegree/Term2/3.Convolutional Neural Networks Videos/datasets/DogResnet50Data.npz')
train_Resnet50 = bottleneck_features_Resnet50['train']
valid_Resnet50 = bottleneck_features_Resnet50['valid']
test_Resnet50 = bottleneck_features_Resnet50['test']
print('-- Obtained -- Resnet50 Bottlenect ---')
bottleneck_features_InceptionV3 = np.load('C:/Training/udacity/AI_NanoDegree/Term2/3.Convolutional Neural Networks Videos/datasets/DogInceptionV3Data.npz')
train_InceptionV3 = bottleneck_features_InceptionV3['train']
valid_InceptionV3 = bottleneck_features_InceptionV3['valid']
test_InceptionV3 = bottleneck_features_InceptionV3['test']
print('-- Obtained -- InceptionV3 Bottlenect ---')
bottleneck_features_Xception = np.load('C:/Training/udacity/AI_NanoDegree/Term2/3.Convolutional Neural Networks Videos/datasets/DogXceptionData.npz')
train_Xception = bottleneck_features_Xception['train']
valid_Xception = bottleneck_features_Xception['valid']
test_Xception = bottleneck_features_Xception['test']
print('-- Obtained -- Xception Bottlenect ---')
-- Obtained -- VGG19 Bottlenect --- -- Obtained -- Resnet50 Bottlenect --- -- Obtained -- InceptionV3 Bottlenect --- -- Obtained -- Xception Bottlenect ---
Create a CNN to classify dog breed. At the end of your code cell block, summarize the layers of your model by executing the line:
<your model's name>.summary()
Question 5: Outline the steps you took to get to your final CNN architecture and your reasoning at each step. Describe why you think the architecture is suitable for the current problem.
Answer:
To decide upon the final CNN architecture I have considered pre-computed keras features VGG16, VGG19 , Resnet50, InceptionV3 and Xception.
Steps that I have considered is
1. Get the bottleneck feature
2. Build the Model Architecture
3. Compile Model
4 . Train the model
5. Load the Model with the Best Validation Loss
6. Test the Model
7. Look for a model with best accuracy
In my analysis Xception , Inception and Resnet provides Test accuracy higher than 60%
Xception Test accuracy: 85.0478%
Inception Test accuracy: 77.1531%
Resnet50 Test accuracy: 81.2201%
Based on the accuracy results I have selected Xception as the best model for this project.
### TODO: Define your architecture.
VGG19_model = Sequential()
VGG19_model.add(GlobalAveragePooling2D(input_shape=train_VGG19.shape[1:]))
VGG19_model.add(Dense(133, activation='softmax'))
VGG19_model.summary()
_________________________________________________________________ Layer (type) Output Shape Param # ================================================================= global_average_pooling2d_3 ( (None, 512) 0 _________________________________________________________________ dense_3 (Dense) (None, 133) 68229 ================================================================= Total params: 68,229.0 Trainable params: 68,229.0 Non-trainable params: 0.0 _________________________________________________________________
### TODO: Define your architecture.
Resnet50_model = Sequential()
Resnet50_model.add(GlobalAveragePooling2D(input_shape=train_Resnet50.shape[1:]))
Resnet50_model.add(Dense(133, activation='softmax'))
Resnet50_model.summary()
_________________________________________________________________ Layer (type) Output Shape Param # ================================================================= global_average_pooling2d_8 ( (None, 2048) 0 _________________________________________________________________ dense_8 (Dense) (None, 133) 272517 ================================================================= Total params: 272,517.0 Trainable params: 272,517.0 Non-trainable params: 0.0 _________________________________________________________________
### TODO: Define your architecture.
InceptionV3_model = Sequential()
InceptionV3_model.add(GlobalAveragePooling2D(input_shape=train_InceptionV3.shape[1:]))
InceptionV3_model.add(Dense(133, activation='softmax'))
InceptionV3_model.summary()
_________________________________________________________________ Layer (type) Output Shape Param # ================================================================= global_average_pooling2d_7 ( (None, 2048) 0 _________________________________________________________________ dense_7 (Dense) (None, 133) 272517 ================================================================= Total params: 272,517.0 Trainable params: 272,517.0 Non-trainable params: 0.0 _________________________________________________________________
### TODO: Define your architecture.
Xception_model = Sequential()
Xception_model.add(GlobalAveragePooling2D(input_shape=train_Xception.shape[1:]))
Xception_model.add(Dense(133, activation='softmax'))
Xception_model.summary()
_________________________________________________________________ Layer (type) Output Shape Param # ================================================================= global_average_pooling2d_9 ( (None, 2048) 0 _________________________________________________________________ dense_9 (Dense) (None, 133) 272517 ================================================================= Total params: 272,517.0 Trainable params: 272,517.0 Non-trainable params: 0.0 _________________________________________________________________
### TODO: Compile the model.
VGG19_model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
print('-- Model Compiled ---')
-- Model Compiled ---
Resnet50_model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
print('-- Model Compiled ---')
-- Model Compiled ---
InceptionV3_model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
print('-- Model Compiled ---')
-- Model Compiled ---
Xception_model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
print('-- Model Compiled ---')
-- Model Compiled ---
Train your model in the code cell below. Use model checkpointing to save the model that attains the best validation loss.
You are welcome to augment the training data, but this is not a requirement.
### TODO: Train the model.
checkpointer_vgg19 = ModelCheckpoint(filepath='weights.best.VGG19.hdf5',
verbose=1, save_best_only=True)
VGG19_model.fit(train_VGG19, train_targets,
validation_data=(valid_VGG19, valid_targets),
epochs=20, batch_size=20, callbacks=[checkpointer_vgg19], verbose=1)
print('---I am done saving model VGG19----')
Train on 6680 samples, validate on 835 samples Epoch 1/20 6640/6680 [============================>.] - ETA: 698s - loss: 15.1269 - acc: 0.0000e+00 - ETA: 392s - loss: 14.7043 - acc: 0.0000e+00 - ETA: 288s - loss: 14.7453 - acc: 0.0000e+00 - ETA: 236s - loss: 14.4638 - acc: 0.0125 - ETA: 212s - loss: 14.5182 - acc: 0.0100 - ETA: 190s - loss: 14.4975 - acc: 0.0083 - ETA: 174s - loss: 14.4742 - acc: 0.0143 - ETA: 161s - loss: 14.4100 - acc: 0.0188 - ETA: 150s - loss: 14.3931 - acc: 0.0222 - ETA: 140s - loss: 14.4761 - acc: 0.0200 - ETA: 133s - loss: 14.4191 - acc: 0.0182 - ETA: 127s - loss: 14.4757 - acc: 0.0167 - ETA: 122s - loss: 14.4309 - acc: 0.0192 - ETA: 117s - loss: 14.3169 - acc: 0.0214 - ETA: 114s - loss: 14.3138 - acc: 0.0233 - ETA: 112s - loss: 14.3176 - acc: 0.0250 - ETA: 108s - loss: 14.2732 - acc: 0.0235 - ETA: 105s - loss: 14.3085 - acc: 0.0222 - ETA: 103s - loss: 14.3015 - acc: 0.0237 - ETA: 101s - loss: 14.3218 - acc: 0.0225 - ETA: 106s - loss: 14.3013 - acc: 0.0238 - ETA: 109s - loss: 14.2505 - acc: 0.0227 - ETA: 107s - loss: 14.2036 - acc: 0.0239 - ETA: 104s - loss: 14.1658 - acc: 0.0229 - ETA: 103s - loss: 14.1015 - acc: 0.0240 - ETA: 101s - loss: 14.0968 - acc: 0.0250 - ETA: 99s - loss: 14.0720 - acc: 0.0241 - ETA: 97s - loss: 14.0687 - acc: 0.0232 - ETA: 96s - loss: 13.9252 - acc: 0.0259 - ETA: 94s - loss: 13.9079 - acc: 0.0250 - ETA: 93s - loss: 13.8352 - acc: 0.0242 - ETA: 91s - loss: 13.7845 - acc: 0.0250 - ETA: 90s - loss: 13.7429 - acc: 0.0242 - ETA: 89s - loss: 13.7076 - acc: 0.0265 - ETA: 88s - loss: 13.6744 - acc: 0.0257 - ETA: 87s - loss: 13.6211 - acc: 0.0264 - ETA: 85s - loss: 13.6071 - acc: 0.0270 - ETA: 84s - loss: 13.6165 - acc: 0.0263 - ETA: 84s - loss: 13.5830 - acc: 0.0269 - ETA: 83s - loss: 13.5986 - acc: 0.0275 - ETA: 82s - loss: 13.6063 - acc: 0.0268 - ETA: 82s - loss: 13.5504 - acc: 0.0298 - ETA: 81s - loss: 13.4874 - acc: 0.0326 - ETA: 80s - loss: 13.4165 - acc: 0.0341 - ETA: 79s - loss: 13.3966 - acc: 0.0344 - ETA: 79s - loss: 13.4509 - acc: 0.0337 - ETA: 78s - loss: 13.4286 - acc: 0.0351 - ETA: 77s - loss: 13.4183 - acc: 0.0365 - ETA: 77s - loss: 13.4004 - acc: 0.0367 - ETA: 76s - loss: 13.3857 - acc: 0.0370 - ETA: 75s - loss: 13.3610 - acc: 0.0373 - ETA: 75s - loss: 13.3253 - acc: 0.0394 - ETA: 74s - loss: 13.3133 - acc: 0.0396 - ETA: 73s - loss: 13.3427 - acc: 0.0389 - ETA: 73s - loss: 13.3187 - acc: 0.0391 - ETA: 72s - loss: 13.3298 - acc: 0.0393 - ETA: 72s - loss: 13.2891 - acc: 0.0412 - ETA: 71s - loss: 13.2900 - acc: 0.0414 - ETA: 71s - loss: 13.2549 - acc: 0.0424 - ETA: 70s - loss: 13.2534 - acc: 0.0425 - ETA: 70s - loss: 13.2351 - acc: 0.0443 - ETA: 69s - loss: 13.2400 - acc: 0.0444 - ETA: 69s - loss: 13.2484 - acc: 0.0437 - ETA: 69s - loss: 13.2109 - acc: 0.0445 - ETA: 68s - loss: 13.1792 - acc: 0.0462 - ETA: 68s - loss: 13.1870 - acc: 0.0455 - ETA: 67s - loss: 13.1926 - acc: 0.0455 - ETA: 67s - loss: 13.1922 - acc: 0.0449 - ETA: 66s - loss: 13.1885 - acc: 0.0442 - ETA: 66s - loss: 13.1861 - acc: 0.0450 - ETA: 66s - 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loss: 10.9650 - acc: 0.1573 - ETA: 10s - loss: 10.9509 - acc: 0.1583 - ETA: 9s - loss: 10.9293 - acc: 0.1589 - ETA: 9s - loss: 10.9393 - acc: 0.1587 - ETA: 9s - loss: 10.9266 - acc: 0.1597 - ETA: 8s - loss: 10.9172 - acc: 0.1612 - ETA: 8s - loss: 10.8956 - acc: 0.1627 - ETA: 7s - loss: 10.8842 - acc: 0.1633 - ETA: 7s - loss: 10.8721 - acc: 0.1639 - ETA: 7s - loss: 10.8713 - acc: 0.1636 - ETA: 7s - loss: 10.8634 - acc: 0.1643 - ETA: 6s - loss: 10.8527 - acc: 0.1652 - ETA: 6s - loss: 10.8480 - acc: 0.1657 - ETA: 5s - loss: 10.8331 - acc: 0.1669 - ETA: 5s - loss: 10.8157 - acc: 0.1681 - ETA: 5s - loss: 10.8045 - acc: 0.1684 - ETA: 4s - loss: 10.8150 - acc: 0.1683 - ETA: 4s - loss: 10.7946 - acc: 0.1694 - ETA: 3s - loss: 10.7871 - acc: 0.1703 - ETA: 3s - loss: 10.7809 - acc: 0.1707 - ETA: 2s - loss: 10.7554 - acc: 0.1723 - ETA: 2s - loss: 10.7601 - acc: 0.1722 - ETA: 2s - loss: 10.7509 - acc: 0.1725 - ETA: 2s - loss: 10.7498 - acc: 0.1729 - ETA: 1s - loss: 10.7351 - acc: 0.1731 - ETA: 1s - loss: 10.7230 - acc: 0.1741 - ETA: 0s - loss: 10.7130 - acc: 0.1751 - ETA: 0s - loss: 10.7161 - acc: 0.1752Epoch 00000: val_loss improved from inf to 9.03461, saving model to weights.best.VGG19.hdf5 6680/6680 [==============================] - 60s - loss: 10.7052 - acc: 0.1756 - val_loss: 9.0346 - val_acc: 0.2838 Epoch 2/20 6560/6680 [============================>.] - ETA: 2s - loss: 9.4185 - acc: 0.3000 - ETA: 2s - loss: 8.3044 - acc: 0.3750 - ETA: 2s - loss: 9.1657 - acc: 0.3357 - ETA: 2s - loss: 8.6526 - acc: 0.3525 - ETA: 2s - loss: 8.6996 - acc: 0.3558 - ETA: 2s - loss: 8.7544 - acc: 0.3516 - ETA: 2s - loss: 8.6030 - acc: 0.3566 - ETA: 2s - loss: 8.7185 - acc: 0.3477 - ETA: 2s - loss: 8.7666 - acc: 0.3360 - ETA: 2s - loss: 8.7228 - acc: 0.3375 - ETA: 2s - loss: 8.7406 - acc: 0.3379 - ETA: 2s - loss: 8.7305 - acc: 0.3412 - ETA: 2s - loss: 8.6772 - acc: 0.3419 - ETA: 2s - loss: 8.6607 - acc: 0.3438 - ETA: 2s - loss: 8.6598 - acc: 0.3424 - ETA: 2s - loss: 8.5614 - acc: 0.3495 - ETA: 1s - loss: 8.6005 - acc: 0.3470 - ETA: 1s - loss: 8.5544 - acc: 0.3509 - ETA: 1s - loss: 8.5559 - acc: 0.3500 - ETA: 1s - loss: 8.5281 - acc: 0.3500 - ETA: 1s - loss: 8.5253 - acc: 0.3512 - ETA: 1s - loss: 8.4466 - acc: 0.3563 - ETA: 1s - loss: 8.4269 - acc: 0.3589 - ETA: 1s - loss: 8.4512 - acc: 0.3578 - ETA: 1s - loss: 8.4226 - acc: 0.3597 - ETA: 1s - loss: 8.4037 - acc: 0.3593 - ETA: 1s - loss: 8.3807 - acc: 0.3586 - ETA: 1s - loss: 8.3353 - acc: 0.3595 - ETA: 1s - loss: 8.3019 - acc: 0.3586 - ETA: 1s - loss: 8.2960 - acc: 0.3593 - ETA: 1s - loss: 8.2874 - acc: 0.3594 - ETA: 1s - loss: 8.2476 - acc: 0.3610 - ETA: 0s - loss: 8.2444 - acc: 0.3611 - ETA: 0s - loss: 8.1877 - acc: 0.3635 - ETA: 0s - loss: 8.1571 - acc: 0.3653 - ETA: 0s - loss: 8.1707 - acc: 0.3659 - ETA: 0s - loss: 8.1289 - acc: 0.3692 - ETA: 0s - loss: 8.1014 - acc: 0.3713 - ETA: 0s - loss: 8.0885 - acc: 0.3703 - ETA: 0s - loss: 8.0828 - acc: 0.3707 - ETA: 0s - loss: 8.0970 - acc: 0.3698 - ETA: 0s - loss: 8.0649 - acc: 0.3716 - ETA: 0s - loss: 8.0637 - acc: 0.3707 - ETA: 0s - loss: 8.0572 - acc: 0.3716 - ETA: 0s - loss: 8.0464 - acc: 0.3729 - ETA: 0s - loss: 8.0240 - acc: 0.3745 - ETA: 0s - loss: 7.9931 - acc: 0.3768 - ETA: 0s - loss: 7.9941 - acc: 0.3766 - ETA: 0s - loss: 7.9828 - acc: 0.3770Epoch 00001: val_loss improved from 9.03461 to 7.83952, saving model to weights.best.VGG19.hdf5 6680/6680 [==============================] - 2s - loss: 7.9819 - acc: 0.3775 - val_loss: 7.8395 - val_acc: 0.3820 Epoch 3/20 6540/6680 [============================>.] - ETA: 2s - loss: 4.3556 - acc: 0.6500 - ETA: 2s - loss: 7.0359 - acc: 0.4667 - ETA: 2s - loss: 7.0886 - acc: 0.4765 - ETA: 2s - loss: 7.3117 - acc: 0.4600 - ETA: 2s - loss: 7.2439 - acc: 0.4656 - ETA: 2s - loss: 7.4731 - acc: 0.4551 - ETA: 2s - loss: 7.3547 - acc: 0.4596 - ETA: 1s - loss: 7.4245 - acc: 0.4583 - ETA: 1s - loss: 7.4699 - acc: 0.4525 - ETA: 1s - loss: 7.4527 - acc: 0.4529 - ETA: 1s - loss: 7.4624 - acc: 0.4547 - ETA: 1s - loss: 7.4146 - acc: 0.4590 - ETA: 1s - loss: 7.3191 - acc: 0.4639 - ETA: 1s - loss: 7.3269 - acc: 0.4593 - ETA: 1s - loss: 7.3762 - acc: 0.4563 - ETA: 1s - loss: 7.3793 - acc: 0.4568 - ETA: 1s - loss: 7.3868 - acc: 0.4572 - ETA: 1s - loss: 7.3540 - acc: 0.4603 - ETA: 1s - loss: 7.3571 - acc: 0.4608 - ETA: 1s - loss: 7.3716 - acc: 0.4613 - ETA: 1s - loss: 7.4036 - acc: 0.4594 - ETA: 1s - loss: 7.3588 - acc: 0.4621 - ETA: 1s - loss: 7.3181 - acc: 0.4652 - ETA: 1s - loss: 7.3154 - acc: 0.4658 - ETA: 1s - loss: 7.3057 - acc: 0.4663 - ETA: 1s - loss: 7.3085 - acc: 0.4661 - ETA: 0s - loss: 7.3184 - acc: 0.4662 - ETA: 0s - loss: 7.3149 - acc: 0.4672 - ETA: 0s - loss: 7.3217 - acc: 0.4675 - ETA: 0s - loss: 7.3051 - acc: 0.4693 - ETA: 0s - loss: 7.3121 - acc: 0.4685 - ETA: 0s - loss: 7.3357 - acc: 0.4662 - ETA: 0s - loss: 7.3602 - acc: 0.4655 - ETA: 0s - loss: 7.3488 - acc: 0.4654 - ETA: 0s - loss: 7.3947 - acc: 0.4621 - ETA: 0s - loss: 7.4048 - acc: 0.4616 - ETA: 0s - loss: 7.4086 - acc: 0.4615 - ETA: 0s - loss: 7.3848 - acc: 0.4624 - ETA: 0s - loss: 7.3719 - acc: 0.4635 - ETA: 0s - loss: 7.3688 - acc: 0.4641 - ETA: 0s - loss: 7.3459 - acc: 0.4644 - ETA: 0s - loss: 7.3305 - acc: 0.4655 - ETA: 0s - loss: 7.3187 - acc: 0.4661 - ETA: 0s - loss: 7.3113 - acc: 0.4663 - ETA: 0s - loss: 7.3075 - acc: 0.4664Epoch 00002: val_loss improved from 7.83952 to 7.70672, saving model to weights.best.VGG19.hdf5 6680/6680 [==============================] - 2s - loss: 7.3252 - acc: 0.4653 - val_loss: 7.7067 - val_acc: 0.4036 Epoch 4/20 6620/6680 [============================>.] - ETA: 2s - loss: 8.1693 - acc: 0.4500 - ETA: 2s - loss: 6.8777 - acc: 0.5333 - ETA: 2s - loss: 7.1652 - acc: 0.5031 - ETA: 2s - loss: 7.3444 - acc: 0.4935 - ETA: 2s - loss: 7.2706 - acc: 0.4967 - ETA: 2s - loss: 7.2079 - acc: 0.4961 - ETA: 2s - loss: 7.1470 - acc: 0.4989 - ETA: 2s - loss: 7.1092 - acc: 0.5038 - ETA: 1s - loss: 7.0195 - acc: 0.5119 - ETA: 1s - loss: 6.9349 - acc: 0.5144 - ETA: 1s - loss: 6.9643 - acc: 0.5130 - ETA: 1s - loss: 7.0114 - acc: 0.5119 - ETA: 1s - loss: 7.0208 - acc: 0.5085 - ETA: 1s - loss: 7.0253 - acc: 0.5089 - ETA: 1s - loss: 7.0290 - acc: 0.5087 - ETA: 1s - loss: 7.0755 - acc: 0.5059 - ETA: 1s - loss: 7.1118 - acc: 0.5017 - ETA: 1s - loss: 7.1334 - acc: 0.5004 - ETA: 1s - loss: 7.1251 - acc: 0.5011 - ETA: 1s - loss: 7.1189 - acc: 0.5018 - ETA: 1s - loss: 7.1396 - acc: 0.5003 - ETA: 1s - loss: 7.0841 - acc: 0.5029 - ETA: 1s - loss: 7.0429 - acc: 0.5058 - ETA: 1s - loss: 7.0359 - acc: 0.5055 - ETA: 1s - loss: 7.0707 - acc: 0.5045 - ETA: 1s - loss: 7.0758 - acc: 0.5048 - ETA: 0s - loss: 7.1042 - acc: 0.5034 - ETA: 0s - loss: 7.1336 - acc: 0.5008 - ETA: 0s - loss: 7.1267 - acc: 0.5012 - ETA: 0s - loss: 7.0932 - acc: 0.5028 - ETA: 0s - loss: 7.0954 - acc: 0.5022 - ETA: 0s - loss: 7.1120 - acc: 0.5006 - ETA: 0s - loss: 7.0726 - acc: 0.5027 - ETA: 0s - loss: 7.0845 - acc: 0.5027 - ETA: 0s - loss: 7.1241 - acc: 0.5000 - ETA: 0s - loss: 7.1156 - acc: 0.5010 - ETA: 0s - loss: 7.1340 - acc: 0.5004 - ETA: 0s - loss: 7.1460 - acc: 0.5004 - ETA: 0s - loss: 7.1171 - acc: 0.5021 - ETA: 0s - loss: 7.0998 - acc: 0.5033 - ETA: 0s - loss: 7.1497 - acc: 0.4998 - ETA: 0s - loss: 7.1361 - acc: 0.5000 - ETA: 0s - loss: 7.1181 - acc: 0.5006 - ETA: 0s - loss: 7.1050 - acc: 0.5014 - ETA: 0s - loss: 7.1056 - acc: 0.5012 - ETA: 0s - loss: 7.0975 - acc: 0.5021Epoch 00003: val_loss improved from 7.70672 to 7.53867, saving model to weights.best.VGG19.hdf5 6680/6680 [==============================] - 2s - loss: 7.1065 - acc: 0.5016 - val_loss: 7.5387 - val_acc: 0.4455 Epoch 5/20 6540/6680 [============================>.] - ETA: 2s - loss: 4.2033 - acc: 0.6500 - ETA: 2s - loss: 6.9566 - acc: 0.5000 - ETA: 2s - loss: 6.7364 - acc: 0.5206 - ETA: 2s - loss: 6.9079 - acc: 0.5167 - ETA: 2s - loss: 6.8997 - acc: 0.5194 - ETA: 2s - loss: 6.7474 - acc: 0.5316 - ETA: 2s - loss: 6.7280 - acc: 0.5333 - ETA: 1s - loss: 6.7450 - acc: 0.5356 - ETA: 1s - loss: 6.6944 - acc: 0.5398 - ETA: 1s - loss: 6.7886 - acc: 0.5351 - ETA: 1s - loss: 6.7489 - acc: 0.5365 - ETA: 1s - loss: 6.7577 - acc: 0.5360 - ETA: 1s - loss: 6.7049 - acc: 0.5399 - ETA: 1s - loss: 6.7513 - acc: 0.5354 - ETA: 1s - loss: 6.8512 - acc: 0.5296 - ETA: 1s - loss: 6.8448 - acc: 0.5305 - ETA: 1s - loss: 6.8451 - acc: 0.5295 - ETA: 1s - loss: 6.8865 - acc: 0.5286 - ETA: 1s - loss: 6.9275 - acc: 0.5267 - ETA: 1s - loss: 6.9178 - acc: 0.5268 - ETA: 1s - loss: 6.8680 - acc: 0.5293 - ETA: 1s - loss: 6.9025 - acc: 0.5276 - ETA: 1s - loss: 6.9380 - acc: 0.5261 - ETA: 1s - loss: 6.9248 - acc: 0.5265 - ETA: 1s - loss: 6.9033 - acc: 0.5269 - ETA: 1s - loss: 6.9093 - acc: 0.5267 - ETA: 1s - loss: 6.8964 - acc: 0.5278 - ETA: 1s - loss: 6.8854 - acc: 0.5289 - ETA: 0s - loss: 6.9390 - acc: 0.5248 - ETA: 0s - loss: 6.9440 - acc: 0.5240 - ETA: 0s - loss: 6.9310 - acc: 0.5247 - ETA: 0s - loss: 6.9219 - acc: 0.5257 - ETA: 0s - loss: 6.9464 - acc: 0.5234 - ETA: 0s - loss: 6.9552 - acc: 0.5226 - ETA: 0s - loss: 6.9194 - acc: 0.5253 - ETA: 0s - loss: 6.9022 - acc: 0.5259 - ETA: 0s - loss: 6.9040 - acc: 0.5261 - ETA: 0s - loss: 6.9107 - acc: 0.5258 - ETA: 0s - loss: 6.9083 - acc: 0.5266 - ETA: 0s - loss: 6.9170 - acc: 0.5265 - ETA: 0s - loss: 6.9355 - acc: 0.5257 - ETA: 0s - loss: 6.9426 - acc: 0.5258 - ETA: 0s - loss: 6.9721 - acc: 0.5245 - ETA: 0s - loss: 6.9793 - acc: 0.5244 - ETA: 0s - loss: 6.9671 - acc: 0.5250 - ETA: 0s - loss: 6.9683 - acc: 0.5242Epoch 00004: val_loss did not improve 6680/6680 [==============================] - 2s - loss: 6.9902 - acc: 0.5228 - val_loss: 7.5614 - val_acc: 0.4503 Epoch 6/20 6660/6680 [============================>.] - ETA: 2s - loss: 5.7051 - acc: 0.6500 - ETA: 2s - loss: 6.6966 - acc: 0.5500 - ETA: 2s - loss: 7.4227 - acc: 0.4969 - ETA: 2s - loss: 7.3269 - acc: 0.5109 - ETA: 2s - loss: 7.1837 - acc: 0.5250 - ETA: 2s - loss: 7.0661 - acc: 0.5324 - ETA: 2s - loss: 7.1328 - acc: 0.5295 - ETA: 2s - loss: 7.1924 - acc: 0.5265 - ETA: 2s - loss: 7.0749 - acc: 0.5284 - ETA: 1s - loss: 7.0852 - acc: 0.5300 - ETA: 1s - loss: 7.0456 - acc: 0.5347 - ETA: 1s - loss: 7.0869 - acc: 0.5304 - ETA: 1s - loss: 6.9769 - acc: 0.5379 - ETA: 1s - loss: 7.0268 - acc: 0.5362 - ETA: 1s - loss: 6.9855 - acc: 0.5391 - ETA: 1s - loss: 6.9845 - acc: 0.5390 - ETA: 1s - loss: 6.9962 - acc: 0.5359 - ETA: 1s - loss: 6.9896 - acc: 0.5360 - ETA: 1s - loss: 7.0240 - acc: 0.5341 - ETA: 1s - loss: 6.9661 - acc: 0.5379 - ETA: 1s - loss: 6.9840 - acc: 0.5368 - ETA: 1s - loss: 6.9711 - acc: 0.5368 - ETA: 1s - loss: 6.9320 - acc: 0.5386 - ETA: 1s - loss: 6.9026 - acc: 0.5402 - ETA: 1s - loss: 6.9144 - acc: 0.5386 - ETA: 1s - loss: 6.8842 - acc: 0.5410 - ETA: 1s - loss: 6.8781 - acc: 0.5424 - ETA: 0s - loss: 6.8701 - acc: 0.5422 - ETA: 0s - loss: 6.8785 - acc: 0.5415 - ETA: 0s - loss: 6.8819 - acc: 0.5410 - ETA: 0s - loss: 6.8782 - acc: 0.5409 - ETA: 0s - loss: 6.8358 - acc: 0.5430 - ETA: 0s - loss: 6.8200 - acc: 0.5434 - ETA: 0s - loss: 6.8348 - acc: 0.5419 - ETA: 0s - loss: 6.8503 - acc: 0.5408 - ETA: 0s - loss: 6.8759 - acc: 0.5387 - ETA: 0s - loss: 6.8823 - acc: 0.5380 - ETA: 0s - loss: 6.8691 - acc: 0.5391 - ETA: 0s - loss: 6.8789 - acc: 0.5388 - ETA: 0s - loss: 6.8643 - acc: 0.5398 - ETA: 0s - loss: 6.8480 - acc: 0.5409 - ETA: 0s - loss: 6.8674 - acc: 0.5399 - ETA: 0s - loss: 6.8455 - acc: 0.5408 - ETA: 0s - loss: 6.8369 - acc: 0.5415 - ETA: 0s - loss: 6.8478 - acc: 0.5411 - ETA: 0s - loss: 6.8621 - acc: 0.5405 - ETA: 0s - loss: 6.8541 - acc: 0.5408Epoch 00005: val_loss improved from 7.53867 to 7.31742, saving model to weights.best.VGG19.hdf5 6680/6680 [==============================] - 2s - loss: 6.8625 - acc: 0.5404 - val_loss: 7.3174 - val_acc: 0.4659 Epoch 7/20 6580/6680 [============================>.] - ETA: 2s - loss: 6.4541 - acc: 0.6000 - ETA: 2s - loss: 6.2565 - acc: 0.5778 - ETA: 2s - loss: 6.6901 - acc: 0.5500 - ETA: 2s - loss: 6.7856 - acc: 0.5438 - ETA: 2s - loss: 6.8102 - acc: 0.5484 - ETA: 2s - loss: 6.8209 - acc: 0.5461 - ETA: 2s - loss: 6.6718 - acc: 0.5543 - ETA: 1s - loss: 6.6422 - acc: 0.5585 - ETA: 1s - loss: 6.7237 - acc: 0.5550 - ETA: 1s - loss: 6.8498 - acc: 0.5478 - ETA: 1s - loss: 6.9137 - acc: 0.5459 - ETA: 1s - loss: 6.9172 - acc: 0.5438 - ETA: 1s - loss: 6.9205 - acc: 0.5443 - ETA: 1s - loss: 6.9825 - acc: 0.5411 - ETA: 1s - loss: 6.9942 - acc: 0.5397 - ETA: 1s - loss: 6.9901 - acc: 0.5399 - ETA: 1s - loss: 6.9870 - acc: 0.5392 - ETA: 1s - loss: 6.9326 - acc: 0.5411 - ETA: 1s - loss: 6.9260 - acc: 0.5408 - ETA: 1s - loss: 6.8954 - acc: 0.5427 - ETA: 1s - loss: 6.8823 - acc: 0.5444 - ETA: 1s - loss: 6.8818 - acc: 0.5437 - ETA: 1s - loss: 6.8972 - acc: 0.5434 - ETA: 1s - loss: 6.9211 - acc: 0.5418 - ETA: 1s - loss: 6.8641 - acc: 0.5448 - ETA: 1s - loss: 6.8753 - acc: 0.5453 - ETA: 1s - loss: 6.8678 - acc: 0.5455 - ETA: 1s - loss: 6.8332 - acc: 0.5477 - ETA: 0s - loss: 6.8497 - acc: 0.5468 - ETA: 0s - loss: 6.8548 - acc: 0.5471 - ETA: 0s - loss: 6.8307 - acc: 0.5495 - ETA: 0s - loss: 6.8376 - acc: 0.5491 - ETA: 0s - loss: 6.8418 - acc: 0.5489 - ETA: 0s - loss: 6.8260 - acc: 0.5502 - ETA: 0s - loss: 6.8383 - acc: 0.5494 - ETA: 0s - loss: 6.8303 - acc: 0.5500 - ETA: 0s - loss: 6.8335 - acc: 0.5500 - ETA: 0s - loss: 6.8071 - acc: 0.5509 - ETA: 0s - loss: 6.7855 - acc: 0.5520 - ETA: 0s - loss: 6.7748 - acc: 0.5527 - ETA: 0s - loss: 6.7856 - acc: 0.5524 - ETA: 0s - loss: 6.7606 - acc: 0.5540 - ETA: 0s - loss: 6.7512 - acc: 0.5544 - ETA: 0s - loss: 6.7615 - acc: 0.5542 - ETA: 0s - loss: 6.7766 - acc: 0.5533 - ETA: 0s - loss: 6.7694 - acc: 0.5538Epoch 00006: val_loss did not improve 6680/6680 [==============================] - 2s - loss: 6.7717 - acc: 0.5534 - val_loss: 7.3525 - val_acc: 0.4659 Epoch 8/20 6580/6680 [============================>.] - ETA: 2s - loss: 8.9788 - acc: 0.4000 - ETA: 2s - loss: 7.0044 - acc: 0.5500 - ETA: 2s - loss: 6.7569 - acc: 0.5667 - ETA: 2s - loss: 6.7802 - acc: 0.5659 - ETA: 2s - loss: 6.4811 - acc: 0.5862 - ETA: 2s - loss: 6.3200 - acc: 0.5919 - ETA: 2s - loss: 6.2149 - acc: 0.5956 - ETA: 2s - loss: 6.2211 - acc: 0.5952 - ETA: 1s - loss: 6.1641 - acc: 0.6000 - ETA: 1s - loss: 6.3187 - acc: 0.5909 - ETA: 1s - loss: 6.4306 - acc: 0.5836 - ETA: 1s - loss: 6.3747 - acc: 0.5869 - ETA: 1s - loss: 6.3350 - acc: 0.5897 - ETA: 1s - loss: 6.4041 - acc: 0.5830 - ETA: 1s - loss: 6.3579 - acc: 0.5842 - ETA: 1s - loss: 6.4206 - acc: 0.5806 - ETA: 1s - loss: 6.4952 - acc: 0.5761 - ETA: 1s - loss: 6.5117 - acc: 0.5754 - ETA: 1s - loss: 6.5418 - acc: 0.5733 - ETA: 1s - loss: 6.5917 - acc: 0.5706 - ETA: 1s - loss: 6.6733 - acc: 0.5647 - ETA: 1s - loss: 6.7333 - acc: 0.5617 - ETA: 1s - loss: 6.7465 - acc: 0.5596 - ETA: 1s - loss: 6.7432 - acc: 0.5598 - ETA: 1s - loss: 6.7760 - acc: 0.5582 - ETA: 1s - loss: 6.7743 - acc: 0.5587 - ETA: 1s - loss: 6.7859 - acc: 0.5576 - ETA: 1s - loss: 6.7844 - acc: 0.5578 - ETA: 0s - loss: 6.7377 - acc: 0.5611 - ETA: 0s - loss: 6.6998 - acc: 0.5626 - ETA: 0s - loss: 6.6923 - acc: 0.5636 - ETA: 0s - loss: 6.6652 - acc: 0.5655 - ETA: 0s - loss: 6.6408 - acc: 0.5675 - ETA: 0s - loss: 6.6360 - acc: 0.5681 - ETA: 0s - loss: 6.6710 - acc: 0.5660 - ETA: 0s - loss: 6.6863 - acc: 0.5650 - ETA: 0s - loss: 6.7347 - acc: 0.5623 - ETA: 0s - loss: 6.7639 - acc: 0.5598 - ETA: 0s - loss: 6.7610 - acc: 0.5599 - ETA: 0s - loss: 6.7684 - acc: 0.5593 - ETA: 0s - loss: 6.7870 - acc: 0.5584 - ETA: 0s - loss: 6.7868 - acc: 0.5582 - ETA: 0s - loss: 6.7705 - acc: 0.5593 - ETA: 0s - loss: 6.7622 - acc: 0.5602 - ETA: 0s - loss: 6.7287 - acc: 0.5622 - ETA: 0s - loss: 6.7278 - acc: 0.5618 - ETA: 0s - loss: 6.7292 - acc: 0.5616Epoch 00007: val_loss did not improve 6680/6680 [==============================] - 2s - loss: 6.7333 - acc: 0.5614 - val_loss: 7.3797 - val_acc: 0.4563 Epoch 9/20 6620/6680 [============================>.] - ETA: 2s - loss: 9.6767 - acc: 0.4000 - ETA: 2s - loss: 7.2570 - acc: 0.5500 - ETA: 2s - loss: 6.7141 - acc: 0.5767 - ETA: 2s - loss: 6.8227 - acc: 0.5705 - ETA: 2s - loss: 6.9586 - acc: 0.5638 - ETA: 2s - loss: 6.6818 - acc: 0.5806 - ETA: 2s - loss: 6.8920 - acc: 0.5674 - ETA: 2s - loss: 6.9281 - acc: 0.5650 - ETA: 2s - loss: 6.8284 - acc: 0.5719 - ETA: 2s - loss: 6.8533 - acc: 0.5695 - ETA: 1s - loss: 6.8554 - acc: 0.5683 - ETA: 1s - loss: 6.8120 - acc: 0.5705 - ETA: 1s - loss: 6.8423 - acc: 0.5682 - ETA: 1s - loss: 6.7983 - acc: 0.5701 - ETA: 1s - loss: 6.7701 - acc: 0.5717 - ETA: 1s - loss: 6.7510 - acc: 0.5731 - ETA: 1s - loss: 6.7062 - acc: 0.5752 - ETA: 1s - loss: 6.7409 - acc: 0.5725 - ETA: 1s - loss: 6.7073 - acc: 0.5742 - ETA: 1s - loss: 6.7743 - acc: 0.5704 - ETA: 1s - loss: 6.7982 - acc: 0.5694 - ETA: 1s - loss: 6.8117 - acc: 0.5681 - ETA: 1s - loss: 6.8125 - acc: 0.5679 - ETA: 1s - loss: 6.7903 - acc: 0.5690 - ETA: 1s - loss: 6.8080 - acc: 0.5676 - ETA: 1s - loss: 6.7838 - acc: 0.5686 - ETA: 1s - loss: 6.7559 - acc: 0.5699 - ETA: 1s - loss: 6.7468 - acc: 0.5704 - ETA: 0s - loss: 6.7417 - acc: 0.5698 - ETA: 0s - loss: 6.7341 - acc: 0.5703 - ETA: 0s - loss: 6.7294 - acc: 0.5698 - ETA: 0s - loss: 6.7223 - acc: 0.5704 - ETA: 0s - loss: 6.7244 - acc: 0.5701 - ETA: 0s - loss: 6.7146 - acc: 0.5707 - ETA: 0s - loss: 6.7112 - acc: 0.5709 - ETA: 0s - loss: 6.6769 - acc: 0.5731 - ETA: 0s - loss: 6.7067 - acc: 0.5713 - ETA: 0s - loss: 6.7159 - acc: 0.5709 - ETA: 0s - loss: 6.6982 - acc: 0.5718 - ETA: 0s - loss: 6.6831 - acc: 0.5725 - ETA: 0s - loss: 6.6807 - acc: 0.5728 - ETA: 0s - loss: 6.6727 - acc: 0.5736 - ETA: 0s - loss: 6.7125 - acc: 0.5713 - ETA: 0s - loss: 6.7122 - acc: 0.5715 - ETA: 0s - loss: 6.7023 - acc: 0.5719 - ETA: 0s - loss: 6.7114 - acc: 0.5711 - ETA: 0s - loss: 6.7092 - acc: 0.5713Epoch 00008: val_loss did not improve 6680/6680 [==============================] - 2s - loss: 6.7007 - acc: 0.5716 - val_loss: 7.3487 - val_acc: 0.4707 Epoch 10/20 6560/6680 [============================>.] - ETA: 2s - loss: 6.4476 - acc: 0.6000 - ETA: 2s - loss: 6.6616 - acc: 0.5813 - ETA: 2s - loss: 6.4150 - acc: 0.5967 - ETA: 2s - loss: 6.5021 - acc: 0.5932 - ETA: 2s - loss: 6.6949 - acc: 0.5776 - ETA: 2s - loss: 6.5855 - acc: 0.5847 - ETA: 2s - loss: 6.5225 - acc: 0.5849 - ETA: 2s - loss: 6.7222 - acc: 0.5740 - ETA: 2s - loss: 6.8335 - acc: 0.5667 - ETA: 1s - loss: 6.7915 - acc: 0.5703 - ETA: 1s - loss: 6.5900 - acc: 0.5824 - ETA: 1s - loss: 6.5542 - acc: 0.5833 - ETA: 1s - loss: 6.5954 - acc: 0.5812 - ETA: 1s - loss: 6.7304 - acc: 0.5728 - ETA: 1s - loss: 6.8094 - acc: 0.5685 - ETA: 1s - loss: 6.8336 - acc: 0.5668 - ETA: 1s - loss: 6.8053 - acc: 0.5684 - ETA: 1s - loss: 6.7795 - acc: 0.5698 - ETA: 1s - loss: 6.7689 - acc: 0.5703 - ETA: 1s - loss: 6.7465 - acc: 0.5719 - ETA: 1s - loss: 6.7385 - acc: 0.5725 - ETA: 1s - loss: 6.7203 - acc: 0.5738 - ETA: 1s - loss: 6.7159 - acc: 0.5734 - ETA: 1s - loss: 6.6974 - acc: 0.5747 - ETA: 1s - loss: 6.7489 - acc: 0.5713 - ETA: 1s - loss: 6.7411 - acc: 0.5707 - ETA: 1s - loss: 6.7241 - acc: 0.5714 - ETA: 1s - loss: 6.6903 - acc: 0.5728 - ETA: 0s - loss: 6.7212 - acc: 0.5714 - ETA: 0s - loss: 6.7694 - acc: 0.5682 - ETA: 0s - loss: 6.7618 - acc: 0.5687 - ETA: 0s - loss: 6.7474 - acc: 0.5692 - ETA: 0s - loss: 6.7309 - acc: 0.5704 - ETA: 0s - loss: 6.7093 - acc: 0.5716 - ETA: 0s - loss: 6.6958 - acc: 0.5723 - ETA: 0s - loss: 6.6975 - acc: 0.5721 - ETA: 0s - loss: 6.7106 - acc: 0.5708 - ETA: 0s - loss: 6.7341 - acc: 0.5695 - ETA: 0s - loss: 6.7122 - acc: 0.5703 - ETA: 0s - loss: 6.7007 - acc: 0.5701 - ETA: 0s - loss: 6.6758 - acc: 0.5713 - ETA: 0s - loss: 6.6831 - acc: 0.5707 - ETA: 0s - loss: 6.6638 - acc: 0.5715 - ETA: 0s - loss: 6.6568 - acc: 0.5713 - ETA: 0s - loss: 6.6471 - acc: 0.5718 - ETA: 0s - loss: 6.6597 - acc: 0.5712Epoch 00009: val_loss improved from 7.31742 to 7.28554, saving model to weights.best.VGG19.hdf5 6680/6680 [==============================] - 2s - loss: 6.6718 - acc: 0.5704 - val_loss: 7.2855 - val_acc: 0.4707 Epoch 11/20 6660/6680 [============================>.] - ETA: 2s - loss: 4.8530 - acc: 0.7000 - ETA: 2s - loss: 7.1603 - acc: 0.5312 - ETA: 2s - loss: 7.1609 - acc: 0.5367 - ETA: 2s - loss: 6.9731 - acc: 0.5523 - ETA: 2s - loss: 6.9980 - acc: 0.5517 - ETA: 2s - loss: 6.8067 - acc: 0.5639 - ETA: 2s - loss: 6.9005 - acc: 0.5593 - ETA: 2s - loss: 6.8004 - acc: 0.5630 - ETA: 2s - loss: 6.8031 - acc: 0.5623 - ETA: 1s - loss: 6.7780 - acc: 0.5648 - ETA: 1s - loss: 6.7581 - acc: 0.5648 - ETA: 1s - loss: 6.6527 - acc: 0.5724 - ETA: 1s - loss: 6.6501 - acc: 0.5724 - ETA: 1s - loss: 6.6484 - acc: 0.5734 - ETA: 1s - loss: 6.5668 - acc: 0.5768 - ETA: 1s - loss: 6.5546 - acc: 0.5774 - ETA: 1s - loss: 6.5610 - acc: 0.5770 - ETA: 1s - loss: 6.5684 - acc: 0.5767 - ETA: 1s - loss: 6.5252 - acc: 0.5783 - ETA: 1s - loss: 6.5543 - acc: 0.5761 - ETA: 1s - loss: 6.5727 - acc: 0.5748 - ETA: 1s - loss: 6.5272 - acc: 0.5774 - ETA: 1s - loss: 6.5589 - acc: 0.5758 - ETA: 1s - loss: 6.5679 - acc: 0.5753 - ETA: 1s - loss: 6.6078 - acc: 0.5729 - ETA: 1s - loss: 6.5657 - acc: 0.5760 - ETA: 1s - loss: 6.5849 - acc: 0.5746 - ETA: 1s - loss: 6.5694 - acc: 0.5751 - ETA: 0s - loss: 6.5581 - acc: 0.5761 - ETA: 0s - loss: 6.6041 - acc: 0.5734 - ETA: 0s - loss: 6.6149 - acc: 0.5729 - ETA: 0s - loss: 6.6077 - acc: 0.5735 - ETA: 0s - loss: 6.5821 - acc: 0.5753 - ETA: 0s - loss: 6.5709 - acc: 0.5757 - ETA: 0s - loss: 6.5830 - acc: 0.5750 - ETA: 0s - loss: 6.5846 - acc: 0.5750 - ETA: 0s - loss: 6.5887 - acc: 0.5750 - ETA: 0s - loss: 6.5973 - acc: 0.5746 - ETA: 0s - loss: 6.6213 - acc: 0.5724 - ETA: 0s - loss: 6.5808 - acc: 0.5743 - ETA: 0s - loss: 6.5782 - acc: 0.5737 - ETA: 0s - loss: 6.5494 - acc: 0.5752 - ETA: 0s - loss: 6.5503 - acc: 0.5752 - ETA: 0s - loss: 6.5534 - acc: 0.5750 - ETA: 0s - loss: 6.5562 - acc: 0.5745 - ETA: 0s - loss: 6.5590 - acc: 0.5747 - ETA: 0s - loss: 6.5476 - acc: 0.5749Epoch 00010: val_loss improved from 7.28554 to 7.19207, saving model to weights.best.VGG19.hdf5 6680/6680 [==============================] - 2s - loss: 6.5329 - acc: 0.5759 - val_loss: 7.1921 - val_acc: 0.4719 Epoch 12/20 6560/6680 [============================>.] - ETA: 2s - loss: 7.2533 - acc: 0.5500 - ETA: 2s - loss: 8.1674 - acc: 0.4812 - ETA: 2s - loss: 7.2200 - acc: 0.5433 - ETA: 2s - loss: 7.0332 - acc: 0.5568 - ETA: 2s - loss: 6.7857 - acc: 0.5724 - ETA: 2s - loss: 6.7798 - acc: 0.5722 - ETA: 2s - loss: 6.5746 - acc: 0.5826 - ETA: 2s - loss: 6.6470 - acc: 0.5780 - ETA: 2s - loss: 6.6205 - acc: 0.5807 - ETA: 2s - loss: 6.5691 - acc: 0.5836 - ETA: 1s - loss: 6.5269 - acc: 0.5859 - ETA: 1s - loss: 6.5525 - acc: 0.5808 - ETA: 1s - loss: 6.5366 - acc: 0.5818 - ETA: 1s - loss: 6.5303 - acc: 0.5810 - ETA: 1s - loss: 6.5181 - acc: 0.5818 - ETA: 1s - loss: 6.4699 - acc: 0.5854 - ETA: 1s - loss: 6.4850 - acc: 0.5841 - ETA: 1s - loss: 6.5629 - acc: 0.5788 - ETA: 1s - loss: 6.5367 - acc: 0.5811 - ETA: 1s - loss: 6.5457 - acc: 0.5802 - ETA: 1s - loss: 6.5184 - acc: 0.5826 - ETA: 1s - loss: 6.4770 - acc: 0.5858 - ETA: 1s - loss: 6.4267 - acc: 0.5884 - ETA: 1s - loss: 6.4195 - acc: 0.5893 - ETA: 1s - loss: 6.4038 - acc: 0.5903 - ETA: 1s - loss: 6.4216 - acc: 0.5893 - ETA: 1s - loss: 6.4178 - acc: 0.5894 - ETA: 1s - loss: 6.4035 - acc: 0.5901 - ETA: 1s - loss: 6.4215 - acc: 0.5894 - ETA: 0s - loss: 6.4684 - acc: 0.5861 - ETA: 0s - loss: 6.4906 - acc: 0.5844 - ETA: 0s - loss: 6.4979 - acc: 0.5839 - ETA: 0s - loss: 6.4943 - acc: 0.5836 - ETA: 0s - loss: 6.4803 - acc: 0.5839 - ETA: 0s - loss: 6.4807 - acc: 0.5836 - ETA: 0s - loss: 6.5044 - acc: 0.5819 - ETA: 0s - loss: 6.5078 - acc: 0.5820 - ETA: 0s - loss: 6.4963 - acc: 0.5828 - ETA: 0s - loss: 6.4692 - acc: 0.5845 - ETA: 0s - loss: 6.4475 - acc: 0.5860 - ETA: 0s - loss: 6.4530 - acc: 0.5857 - ETA: 0s - loss: 6.4548 - acc: 0.5854 - ETA: 0s - loss: 6.4249 - acc: 0.5871 - ETA: 0s - loss: 6.4194 - acc: 0.5874 - ETA: 0s - loss: 6.4140 - acc: 0.5872 - ETA: 0s - loss: 6.3838 - acc: 0.5890Epoch 00011: val_loss improved from 7.19207 to 7.14064, saving model to weights.best.VGG19.hdf5 6680/6680 [==============================] - 2s - loss: 6.3888 - acc: 0.5886 - val_loss: 7.1406 - val_acc: 0.4623 Epoch 13/20 6540/6680 [============================>.] - ETA: 2s - loss: 6.4485 - acc: 0.6000 - ETA: 2s - loss: 7.2638 - acc: 0.5500 - ETA: 2s - loss: 7.0688 - acc: 0.5500 - ETA: 2s - loss: 6.7135 - acc: 0.5659 - ETA: 2s - loss: 6.5108 - acc: 0.5724 - ETA: 2s - loss: 6.4742 - acc: 0.5778 - ETA: 2s - loss: 6.4875 - acc: 0.5779 - ETA: 2s - loss: 6.4018 - acc: 0.5820 - ETA: 2s - loss: 6.3713 - acc: 0.5833 - ETA: 1s - loss: 6.3204 - acc: 0.5859 - ETA: 1s - loss: 6.2626 - acc: 0.5894 - ETA: 1s - loss: 6.2010 - acc: 0.5929 - ETA: 1s - loss: 6.1842 - acc: 0.5924 - ETA: 1s - loss: 6.2301 - acc: 0.5875 - ETA: 1s - loss: 6.2299 - acc: 0.5879 - ETA: 1s - loss: 6.1705 - acc: 0.5915 - ETA: 1s - loss: 6.2412 - acc: 0.5867 - ETA: 1s - loss: 6.2400 - acc: 0.5854 - ETA: 1s - loss: 6.2596 - acc: 0.5854 - ETA: 1s - loss: 6.2511 - acc: 0.5866 - ETA: 1s - loss: 6.2301 - acc: 0.5887 - ETA: 1s - loss: 6.2270 - acc: 0.5892 - ETA: 1s - loss: 6.1988 - acc: 0.5913 - ETA: 1s - loss: 6.2259 - acc: 0.5901 - ETA: 1s - loss: 6.2148 - acc: 0.5902 - ETA: 1s - loss: 6.2369 - acc: 0.5889 - ETA: 1s - loss: 6.2294 - acc: 0.5891 - ETA: 1s - loss: 6.2250 - acc: 0.5895 - ETA: 1s - loss: 6.2366 - acc: 0.5886 - ETA: 0s - loss: 6.2398 - acc: 0.5887 - ETA: 0s - loss: 6.2668 - acc: 0.5877 - ETA: 0s - loss: 6.2475 - acc: 0.5892 - ETA: 0s - loss: 6.2617 - acc: 0.5882 - ETA: 0s - loss: 6.2595 - acc: 0.5888 - ETA: 0s - loss: 6.2535 - acc: 0.5889 - ETA: 0s - loss: 6.2527 - acc: 0.5892 - ETA: 0s - loss: 6.2648 - acc: 0.5883 - ETA: 0s - loss: 6.2151 - acc: 0.5915 - ETA: 0s - loss: 6.2328 - acc: 0.5904 - ETA: 0s - loss: 6.2152 - acc: 0.5918 - ETA: 0s - loss: 6.2019 - acc: 0.5929 - ETA: 0s - loss: 6.2234 - acc: 0.5913 - ETA: 0s - loss: 6.1938 - acc: 0.5936 - ETA: 0s - loss: 6.2053 - acc: 0.5926 - ETA: 0s - loss: 6.1906 - acc: 0.5939 - ETA: 0s - loss: 6.1780 - acc: 0.5950 - ETA: 0s - loss: 6.1864 - acc: 0.5945Epoch 00012: val_loss improved from 7.14064 to 6.96708, saving model to weights.best.VGG19.hdf5 6680/6680 [==============================] - 2s - loss: 6.2003 - acc: 0.5934 - val_loss: 6.9671 - val_acc: 0.4922 Epoch 14/20 6580/6680 [============================>.] - ETA: 2s - loss: 4.0318 - acc: 0.7500 - ETA: 2s - loss: 5.6627 - acc: 0.6389 - ETA: 2s - loss: 6.0738 - acc: 0.6147 - ETA: 2s - loss: 6.2663 - acc: 0.5979 - ETA: 2s - loss: 6.2076 - acc: 0.6016 - ETA: 2s - loss: 6.1520 - acc: 0.6038 - ETA: 2s - loss: 6.1807 - acc: 0.6021 - ETA: 1s - loss: 6.1734 - acc: 0.6037 - ETA: 1s - loss: 6.2135 - acc: 0.6016 - ETA: 1s - loss: 6.1295 - acc: 0.6065 - ETA: 1s - loss: 6.0458 - acc: 0.6105 - ETA: 1s - loss: 6.1729 - acc: 0.6030 - ETA: 1s - loss: 6.1594 - acc: 0.6050 - ETA: 1s - loss: 6.1656 - acc: 0.6046 - ETA: 1s - loss: 6.1203 - acc: 0.6067 - ETA: 1s - loss: 6.1290 - acc: 0.6059 - ETA: 1s - loss: 6.1366 - acc: 0.6051 - ETA: 1s - loss: 6.0973 - acc: 0.6076 - ETA: 1s - loss: 6.1346 - acc: 0.6061 - ETA: 1s - loss: 6.1299 - acc: 0.6065 - ETA: 1s - loss: 6.1396 - acc: 0.6055 - ETA: 1s - loss: 6.1195 - acc: 0.6069 - ETA: 1s - loss: 6.0895 - acc: 0.6088 - ETA: 1s - loss: 6.0725 - acc: 0.6090 - ETA: 1s - loss: 6.0662 - acc: 0.6092 - ETA: 1s - loss: 6.0370 - acc: 0.6115 - ETA: 1s - loss: 6.0281 - acc: 0.6124 - ETA: 1s - loss: 6.0449 - acc: 0.6112 - ETA: 0s - loss: 6.0813 - acc: 0.6089 - ETA: 0s - loss: 6.0907 - acc: 0.6086 - ETA: 0s - loss: 6.0765 - acc: 0.6099 - ETA: 0s - loss: 6.0727 - acc: 0.6096 - ETA: 0s - loss: 6.0884 - acc: 0.6087 - ETA: 0s - loss: 6.1221 - acc: 0.6063 - ETA: 0s - loss: 6.1125 - acc: 0.6065 - ETA: 0s - loss: 6.1444 - acc: 0.6050 - ETA: 0s - loss: 6.1175 - acc: 0.6062 - ETA: 0s - loss: 6.1136 - acc: 0.6062 - ETA: 0s - loss: 6.1154 - acc: 0.6060 - ETA: 0s - loss: 6.1084 - acc: 0.6064 - ETA: 0s - loss: 6.0879 - acc: 0.6075 - ETA: 0s - loss: 6.0868 - acc: 0.6077 - ETA: 0s - loss: 6.0725 - acc: 0.6085 - ETA: 0s - loss: 6.0796 - acc: 0.6071 - ETA: 0s - loss: 6.0605 - acc: 0.6086 - ETA: 0s - loss: 6.0623 - acc: 0.6085 - ETA: 0s - loss: 6.0799 - acc: 0.6073Epoch 00013: val_loss improved from 6.96708 to 6.90283, saving model to weights.best.VGG19.hdf5 6680/6680 [==============================] - 2s - loss: 6.0955 - acc: 0.6063 - val_loss: 6.9028 - val_acc: 0.4946 Epoch 15/20 6640/6680 [============================>.] - ETA: 2s - loss: 6.5341 - acc: 0.5500 - ETA: 2s - loss: 6.7830 - acc: 0.5625 - ETA: 2s - loss: 6.4974 - acc: 0.5767 - ETA: 2s - loss: 6.1607 - acc: 0.6000 - ETA: 2s - loss: 6.0086 - acc: 0.6086 - ETA: 2s - loss: 5.9323 - acc: 0.6139 - ETA: 2s - loss: 5.8319 - acc: 0.6221 - ETA: 2s - loss: 5.9466 - acc: 0.6160 - ETA: 2s - loss: 5.9876 - acc: 0.6132 - ETA: 2s - loss: 5.9460 - acc: 0.6164 - ETA: 1s - loss: 5.9303 - acc: 0.6176 - ETA: 1s - loss: 5.8492 - acc: 0.6209 - ETA: 1s - loss: 5.9007 - acc: 0.6172 - ETA: 1s - loss: 5.8693 - acc: 0.6189 - ETA: 1s - loss: 5.8646 - acc: 0.6194 - ETA: 1s - loss: 5.8897 - acc: 0.6182 - ETA: 1s - loss: 5.8242 - acc: 0.6220 - ETA: 1s - loss: 5.9051 - acc: 0.6176 - ETA: 1s - loss: 5.9373 - acc: 0.6163 - ETA: 1s - loss: 5.9325 - acc: 0.6154 - ETA: 1s - loss: 5.9504 - acc: 0.6136 - ETA: 1s - loss: 5.9162 - acc: 0.6165 - ETA: 1s - loss: 5.9279 - acc: 0.6151 - ETA: 1s - loss: 5.9604 - acc: 0.6129 - ETA: 1s - loss: 5.9647 - acc: 0.6127 - ETA: 1s - loss: 5.9629 - acc: 0.6124 - ETA: 1s - loss: 5.9857 - acc: 0.6112 - ETA: 0s - loss: 6.0692 - acc: 0.6060 - ETA: 0s - loss: 6.0713 - acc: 0.6061 - ETA: 0s - loss: 6.0625 - acc: 0.6063 - ETA: 0s - loss: 6.0309 - acc: 0.6084 - ETA: 0s - loss: 6.0380 - acc: 0.6077 - ETA: 0s - loss: 6.0347 - acc: 0.6081 - ETA: 0s - loss: 6.0594 - acc: 0.6064 - ETA: 0s - loss: 6.0683 - acc: 0.6058 - ETA: 0s - loss: 6.0563 - acc: 0.6067 - ETA: 0s - loss: 6.0331 - acc: 0.6086 - ETA: 0s - loss: 6.0343 - acc: 0.6086 - ETA: 0s - loss: 6.0292 - acc: 0.6089 - ETA: 0s - loss: 5.9970 - acc: 0.6104 - ETA: 0s - loss: 5.9836 - acc: 0.6116 - ETA: 0s - loss: 5.9653 - acc: 0.6121 - ETA: 0s - loss: 5.9798 - acc: 0.6113 - ETA: 0s - loss: 5.9535 - acc: 0.6132 - ETA: 0s - loss: 5.9545 - acc: 0.6132 - ETA: 0s - loss: 5.9593 - acc: 0.6129 - ETA: 0s - loss: 5.9504 - acc: 0.6139Epoch 00014: val_loss improved from 6.90283 to 6.77371, saving model to weights.best.VGG19.hdf5 6680/6680 [==============================] - 2s - loss: 5.9538 - acc: 0.6135 - val_loss: 6.7737 - val_acc: 0.4934 Epoch 16/20 6600/6680 [============================>.] - ETA: 2s - loss: 4.8368 - acc: 0.7000 - ETA: 2s - loss: 5.9563 - acc: 0.6250 - ETA: 2s - loss: 5.4972 - acc: 0.6500 - ETA: 2s - loss: 5.7275 - acc: 0.6386 - ETA: 2s - loss: 5.7081 - acc: 0.6414 - ETA: 2s - loss: 5.6594 - acc: 0.6389 - ETA: 2s - loss: 5.4548 - acc: 0.6512 - ETA: 2s - loss: 5.5913 - acc: 0.6420 - ETA: 2s - loss: 5.7438 - acc: 0.6333 - ETA: 2s - loss: 5.7712 - acc: 0.6328 - ETA: 1s - loss: 5.8923 - acc: 0.6239 - ETA: 1s - loss: 5.8970 - acc: 0.6224 - ETA: 1s - loss: 5.9446 - acc: 0.6188 - ETA: 1s - loss: 5.9168 - acc: 0.6201 - ETA: 1s - loss: 5.9096 - acc: 0.6197 - ETA: 1s - loss: 5.9154 - acc: 0.6193 - ETA: 1s - loss: 5.9380 - acc: 0.6177 - ETA: 1s - loss: 5.9020 - acc: 0.6200 - ETA: 1s - loss: 5.8457 - acc: 0.6236 - ETA: 1s - loss: 5.9021 - acc: 0.6205 - ETA: 1s - loss: 5.8932 - acc: 0.6213 - ETA: 1s - loss: 5.9121 - acc: 0.6203 - ETA: 1s - loss: 5.9238 - acc: 0.6194 - ETA: 1s - loss: 5.9123 - acc: 0.6204 - ETA: 1s - loss: 5.9412 - acc: 0.6186 - ETA: 1s - loss: 5.9054 - acc: 0.6210 - ETA: 1s - loss: 5.9116 - acc: 0.6208 - ETA: 1s - loss: 5.9065 - acc: 0.6216 - ETA: 1s - loss: 5.9390 - acc: 0.6195 - ETA: 0s - loss: 5.9142 - acc: 0.6211 - ETA: 0s - loss: 5.8684 - acc: 0.6239 - ETA: 0s - loss: 5.8679 - acc: 0.6241 - ETA: 0s - loss: 5.8825 - acc: 0.6233 - ETA: 0s - loss: 5.8983 - acc: 0.6224 - ETA: 0s - loss: 5.8877 - acc: 0.6225 - ETA: 0s - loss: 5.9102 - acc: 0.6211 - ETA: 0s - loss: 5.9033 - acc: 0.6217 - ETA: 0s - loss: 5.9147 - acc: 0.6210 - ETA: 0s - loss: 5.9031 - acc: 0.6213 - ETA: 0s - loss: 5.9128 - acc: 0.6206 - ETA: 0s - loss: 5.9084 - acc: 0.6211 - ETA: 0s - loss: 5.9077 - acc: 0.6212 - ETA: 0s - loss: 5.9046 - acc: 0.6215 - ETA: 0s - loss: 5.8806 - acc: 0.6226 - ETA: 0s - loss: 5.8795 - acc: 0.6224 - ETA: 0s - loss: 5.8772 - acc: 0.6222 - ETA: 0s - loss: 5.8633 - acc: 0.6229Epoch 00015: val_loss did not improve 6680/6680 [==============================] - 2s - loss: 5.8557 - acc: 0.6231 - val_loss: 6.7866 - val_acc: 0.4802 Epoch 17/20 6540/6680 [============================>.] - ETA: 2s - loss: 4.8449 - acc: 0.7000 - ETA: 2s - loss: 5.2793 - acc: 0.6611 - ETA: 2s - loss: 5.7901 - acc: 0.6312 - ETA: 2s - loss: 5.6799 - acc: 0.6396 - ETA: 2s - loss: 5.7085 - acc: 0.6359 - ETA: 2s - loss: 5.8191 - acc: 0.6300 - ETA: 1s - loss: 5.8822 - acc: 0.6255 - ETA: 1s - loss: 5.8433 - acc: 0.6259 - ETA: 1s - loss: 5.8998 - acc: 0.6238 - ETA: 1s - loss: 5.9920 - acc: 0.6191 - ETA: 1s - loss: 5.8876 - acc: 0.6260 - ETA: 1s - loss: 5.8577 - acc: 0.6280 - ETA: 1s - loss: 5.8453 - acc: 0.6287 - ETA: 1s - loss: 5.7454 - acc: 0.6344 - ETA: 1s - loss: 5.6916 - acc: 0.6383 - ETA: 1s - loss: 5.7329 - acc: 0.6364 - ETA: 1s - loss: 5.8036 - acc: 0.6325 - ETA: 1s - loss: 5.8028 - acc: 0.6310 - ETA: 1s - loss: 5.8865 - acc: 0.6263 - ETA: 1s - loss: 5.8458 - acc: 0.6290 - ETA: 1s - loss: 5.8696 - acc: 0.6276 - ETA: 1s - loss: 5.9230 - acc: 0.6247 - ETA: 1s - loss: 5.9369 - acc: 0.6239 - ETA: 1s - loss: 5.9498 - acc: 0.6229 - ETA: 1s - loss: 5.9562 - acc: 0.6228 - ETA: 1s - loss: 5.9173 - acc: 0.6256 - ETA: 1s - loss: 5.9339 - acc: 0.6241 - ETA: 1s - loss: 5.9414 - acc: 0.6237 - ETA: 0s - loss: 5.9275 - acc: 0.6244 - ETA: 0s - loss: 5.9219 - acc: 0.6250 - ETA: 0s - loss: 5.9434 - acc: 0.6235 - ETA: 0s - loss: 5.9414 - acc: 0.6236 - ETA: 0s - loss: 5.9556 - acc: 0.6225 - ETA: 0s - loss: 5.9560 - acc: 0.6222 - ETA: 0s - loss: 5.9423 - acc: 0.6228 - ETA: 0s - loss: 5.9256 - acc: 0.6240 - ETA: 0s - loss: 5.8999 - acc: 0.6257 - ETA: 0s - loss: 5.8597 - acc: 0.6280 - ETA: 0s - loss: 5.8675 - acc: 0.6273 - ETA: 0s - loss: 5.8408 - acc: 0.6288 - ETA: 0s - loss: 5.8254 - acc: 0.6296 - ETA: 0s - loss: 5.8409 - acc: 0.6288 - ETA: 0s - loss: 5.8177 - acc: 0.6303 - ETA: 0s - loss: 5.8359 - acc: 0.6291 - ETA: 0s - loss: 5.8104 - acc: 0.6302 - ETA: 0s - loss: 5.8068 - acc: 0.6300 - ETA: 0s - loss: 5.8113 - acc: 0.6298Epoch 00016: val_loss improved from 6.77371 to 6.73259, saving model to weights.best.VGG19.hdf5 6680/6680 [==============================] - 2s - loss: 5.8259 - acc: 0.6290 - val_loss: 6.7326 - val_acc: 0.5018 Epoch 18/20 6560/6680 [============================>.] - ETA: 2s - loss: 4.8489 - acc: 0.7000 - ETA: 2s - loss: 6.6621 - acc: 0.5750 - ETA: 2s - loss: 6.3497 - acc: 0.6000 - ETA: 2s - loss: 6.4258 - acc: 0.5955 - ETA: 2s - loss: 6.2923 - acc: 0.6052 - ETA: 2s - loss: 6.2106 - acc: 0.6111 - ETA: 2s - loss: 6.0805 - acc: 0.6198 - ETA: 2s - loss: 6.0065 - acc: 0.6240 - ETA: 2s - loss: 5.9357 - acc: 0.6281 - ETA: 2s - loss: 5.8964 - acc: 0.6289 - ETA: 2s - loss: 5.8611 - acc: 0.6317 - ETA: 1s - loss: 5.8011 - acc: 0.6348 - ETA: 1s - loss: 5.6953 - acc: 0.6413 - ETA: 1s - loss: 5.6485 - acc: 0.6446 - ETA: 1s - loss: 5.6341 - acc: 0.6455 - ETA: 1s - loss: 5.6424 - acc: 0.6454 - ETA: 1s - loss: 5.6654 - acc: 0.6435 - ETA: 1s - loss: 5.6789 - acc: 0.6427 - ETA: 1s - loss: 5.6905 - acc: 0.6416 - ETA: 1s - loss: 5.6190 - acc: 0.6457 - ETA: 1s - loss: 5.6331 - acc: 0.6445 - ETA: 1s - loss: 5.6456 - acc: 0.6435 - ETA: 1s - loss: 5.6631 - acc: 0.6425 - ETA: 1s - loss: 5.6240 - acc: 0.6452 - ETA: 1s - loss: 5.6268 - acc: 0.6440 - ETA: 1s - loss: 5.6594 - acc: 0.6418 - ETA: 1s - loss: 5.6994 - acc: 0.6392 - ETA: 0s - loss: 5.7048 - acc: 0.6386 - ETA: 0s - loss: 5.7074 - acc: 0.6383 - ETA: 0s - loss: 5.7019 - acc: 0.6388 - ETA: 0s - loss: 5.6966 - acc: 0.6394 - ETA: 0s - loss: 5.7306 - acc: 0.6373 - ETA: 0s - loss: 5.7256 - acc: 0.6373 - ETA: 0s - loss: 5.7371 - acc: 0.6364 - ETA: 0s - loss: 5.7020 - acc: 0.6388 - ETA: 0s - loss: 5.7346 - acc: 0.6368 - ETA: 0s - loss: 5.7413 - acc: 0.6366 - ETA: 0s - loss: 5.7549 - acc: 0.6358 - ETA: 0s - loss: 5.7557 - acc: 0.6357 - ETA: 0s - loss: 5.7392 - acc: 0.6367 - ETA: 0s - loss: 5.7404 - acc: 0.6363 - ETA: 0s - loss: 5.7598 - acc: 0.6353 - ETA: 0s - loss: 5.7652 - acc: 0.6350 - ETA: 0s - loss: 5.7667 - acc: 0.6347 - ETA: 0s - loss: 5.7741 - acc: 0.6344 - ETA: 0s - loss: 5.7790 - acc: 0.6341Epoch 00017: val_loss improved from 6.73259 to 6.72861, saving model to weights.best.VGG19.hdf5 6680/6680 [==============================] - 2s - loss: 5.8033 - acc: 0.6326 - val_loss: 6.7286 - val_acc: 0.5102 Epoch 19/20 6580/6680 [============================>.] - ETA: 2s - loss: 7.2536 - acc: 0.5500 - ETA: 2s - loss: 6.2526 - acc: 0.6062 - ETA: 2s - loss: 6.5054 - acc: 0.5933 - ETA: 2s - loss: 6.3045 - acc: 0.6068 - ETA: 2s - loss: 6.0057 - acc: 0.6259 - ETA: 2s - loss: 6.0114 - acc: 0.6250 - ETA: 2s - loss: 6.0084 - acc: 0.6256 - ETA: 2s - loss: 5.7993 - acc: 0.6388 - ETA: 2s - loss: 5.7879 - acc: 0.6384 - ETA: 2s - loss: 5.8245 - acc: 0.6357 - ETA: 2s - loss: 5.8442 - acc: 0.6343 - ETA: 2s - loss: 5.8279 - acc: 0.6344 - ETA: 1s - loss: 5.8812 - acc: 0.6304 - ETA: 1s - loss: 5.9000 - acc: 0.6291 - ETA: 1s - loss: 5.8489 - acc: 0.6327 - ETA: 1s - loss: 5.8829 - acc: 0.6305 - ETA: 1s - loss: 5.9047 - acc: 0.6290 - ETA: 1s - loss: 5.8625 - acc: 0.6315 - ETA: 1s - loss: 5.8376 - acc: 0.6333 - ETA: 1s - loss: 5.8218 - acc: 0.6342 - ETA: 1s - loss: 5.8076 - acc: 0.6350 - ETA: 1s - loss: 5.8054 - acc: 0.6354 - ETA: 1s - loss: 5.8242 - acc: 0.6341 - ETA: 1s - loss: 5.8863 - acc: 0.6304 - ETA: 1s - loss: 5.8525 - acc: 0.6324 - ETA: 1s - loss: 5.8167 - acc: 0.6349 - ETA: 1s - loss: 5.7801 - acc: 0.6368 - ETA: 1s - loss: 5.7284 - acc: 0.6399 - ETA: 1s - loss: 5.7587 - acc: 0.6380 - ETA: 1s - loss: 5.7472 - acc: 0.6387 - ETA: 0s - loss: 5.7514 - acc: 0.6386 - ETA: 0s - loss: 5.7516 - acc: 0.6387 - ETA: 0s - loss: 5.7424 - acc: 0.6393 - ETA: 0s - loss: 5.7432 - acc: 0.6392 - ETA: 0s - loss: 5.7343 - acc: 0.6395 - ETA: 0s - loss: 5.7417 - acc: 0.6392 - ETA: 0s - loss: 5.7389 - acc: 0.6395 - ETA: 0s - loss: 5.7510 - acc: 0.6384 - ETA: 0s - loss: 5.7820 - acc: 0.6366 - ETA: 0s - loss: 5.7885 - acc: 0.6362 - ETA: 0s - loss: 5.7851 - acc: 0.6364 - ETA: 0s - loss: 5.7789 - acc: 0.6369 - ETA: 0s - loss: 5.7812 - acc: 0.6369 - ETA: 0s - loss: 5.7818 - acc: 0.6363 - ETA: 0s - loss: 5.7818 - acc: 0.6362 - ETA: 0s - loss: 5.7741 - acc: 0.6366 - ETA: 0s - loss: 5.7663 - acc: 0.6370 - ETA: 0s - loss: 5.7951 - acc: 0.6353Epoch 00018: val_loss did not improve 6680/6680 [==============================] - 2s - loss: 5.7952 - acc: 0.6353 - val_loss: 6.7359 - val_acc: 0.5078 Epoch 20/20 6660/6680 [============================>.] - ETA: 2s - loss: 6.4483 - acc: 0.6000 - ETA: 2s - loss: 5.8747 - acc: 0.6312 - ETA: 2s - loss: 5.6598 - acc: 0.6467 - ETA: 2s - loss: 5.8374 - acc: 0.6364 - ETA: 2s - loss: 5.9014 - acc: 0.6328 - ETA: 2s - loss: 5.9978 - acc: 0.6270 - ETA: 2s - loss: 5.8129 - acc: 0.6386 - ETA: 2s - loss: 5.7894 - acc: 0.6402 - ETA: 1s - loss: 5.6879 - acc: 0.6466 - ETA: 1s - loss: 5.6588 - acc: 0.6485 - ETA: 1s - loss: 5.6683 - acc: 0.6480 - ETA: 1s - loss: 5.7082 - acc: 0.6451 - ETA: 1s - loss: 5.7317 - acc: 0.6432 - ETA: 1s - loss: 5.7760 - acc: 0.6405 - ETA: 1s - loss: 5.7825 - acc: 0.6398 - ETA: 1s - loss: 5.7809 - acc: 0.6400 - ETA: 1s - loss: 5.7718 - acc: 0.6407 - ETA: 1s - loss: 5.8484 - acc: 0.6360 - ETA: 1s - loss: 5.8743 - acc: 0.6345 - ETA: 1s - loss: 5.8974 - acc: 0.6331 - ETA: 1s - loss: 5.9129 - acc: 0.6322 - ETA: 1s - loss: 5.8964 - acc: 0.6330 - ETA: 1s - loss: 5.9509 - acc: 0.6297 - ETA: 1s - loss: 5.9387 - acc: 0.6299 - ETA: 1s - loss: 5.9285 - acc: 0.6306 - ETA: 1s - loss: 5.9429 - acc: 0.6292 - ETA: 1s - loss: 5.9608 - acc: 0.6276 - ETA: 0s - loss: 5.9545 - acc: 0.6279 - ETA: 0s - loss: 5.9095 - acc: 0.6308 - ETA: 0s - loss: 5.9242 - acc: 0.6298 - ETA: 0s - loss: 5.8855 - acc: 0.6323 - ETA: 0s - loss: 5.8491 - acc: 0.6346 - ETA: 0s - loss: 5.8292 - acc: 0.6357 - ETA: 0s - loss: 5.8127 - acc: 0.6363 - ETA: 0s - loss: 5.7917 - acc: 0.6378 - ETA: 0s - loss: 5.8227 - acc: 0.6359 - ETA: 0s - loss: 5.8534 - acc: 0.6339 - ETA: 0s - loss: 5.7925 - acc: 0.6374 - ETA: 0s - loss: 5.7795 - acc: 0.6378 - ETA: 0s - loss: 5.7950 - acc: 0.6368 - ETA: 0s - loss: 5.7993 - acc: 0.6363 - ETA: 0s - loss: 5.8122 - acc: 0.6356 - ETA: 0s - loss: 5.7920 - acc: 0.6366 - ETA: 0s - loss: 5.7849 - acc: 0.6369 - ETA: 0s - loss: 5.7492 - acc: 0.6389 - ETA: 0s - loss: 5.7653 - acc: 0.6378 - ETA: 0s - loss: 5.7805 - acc: 0.6367 - ETA: 0s - loss: 5.7801 - acc: 0.6368Epoch 00019: val_loss improved from 6.72861 to 6.69299, saving model to weights.best.VGG19.hdf5 6680/6680 [==============================] - 2s - loss: 5.7797 - acc: 0.6368 - val_loss: 6.6930 - val_acc: 0.5030 ---I am done saving model VGG19----
### TODO: Train the model.
checkpointer_Resnet50 = ModelCheckpoint(filepath='weights.best.Resnet50.hdf5',
verbose=1, save_best_only=True)
Resnet50_model.fit(train_Resnet50, train_targets,
validation_data=(valid_Resnet50, valid_targets),
epochs=20, batch_size=20, callbacks=[checkpointer_Resnet50], verbose=1)
print('---I am done saving model valid_Resnet50 ----')
Train on 6680 samples, validate on 835 samples Epoch 1/20 6660/6680 [============================>.] - ETA: 444s - loss: 5.5372 - acc: 0.0000e+00 - ETA: 254s - loss: 6.2062 - acc: 0.0000e+00 - ETA: 192s - loss: 6.1238 - acc: 0.0000e+00 - ETA: 156s - loss: 6.1489 - acc: 0.0000e+00 - ETA: 136s - loss: 6.0100 - acc: 0.0100 - ETA: 122s - loss: 6.1365 - acc: 0.0167 - ETA: 113s - loss: 5.9950 - acc: 0.0214 - ETA: 105s - loss: 5.9432 - acc: 0.0250 - ETA: 97s - loss: 5.8589 - acc: 0.0278 - ETA: 93s - loss: 5.8087 - acc: 0.0350 - ETA: 88s - loss: 5.7267 - acc: 0.0409 - ETA: 83s - loss: 5.6160 - acc: 0.0458 - ETA: 79s - loss: 5.5145 - acc: 0.0538 - ETA: 76s - loss: 5.4653 - acc: 0.0607 - ETA: 74s - loss: 5.4222 - acc: 0.0633 - ETA: 70s - loss: 5.3523 - acc: 0.0719 - ETA: 69s - loss: 5.3008 - acc: 0.0735 - ETA: 67s - loss: 5.2598 - acc: 0.0750 - ETA: 65s - loss: 5.1933 - acc: 0.0789 - ETA: 63s - loss: 5.1840 - acc: 0.0850 - ETA: 62s - loss: 5.1180 - acc: 0.0857 - ETA: 61s - loss: 5.0712 - acc: 0.0909 - ETA: 60s - loss: 5.0306 - acc: 0.0935 - ETA: 59s - loss: 4.9681 - acc: 0.0979 - ETA: 57s - loss: 4.9047 - acc: 0.1020 - ETA: 56s - loss: 4.8512 - acc: 0.1096 - ETA: 55s - loss: 4.7863 - acc: 0.1222 - ETA: 55s - loss: 4.7349 - acc: 0.1196 - ETA: 54s - loss: 4.6977 - acc: 0.1207 - ETA: 53s - loss: 4.6440 - acc: 0.1317 - ETA: 52s - loss: 4.5972 - acc: 0.1355 - ETA: 51s - loss: 4.5620 - acc: 0.1391 - ETA: 50s - loss: 4.5083 - acc: 0.1424 - ETA: 49s - loss: 4.4786 - acc: 0.1471 - ETA: 49s - loss: 4.4803 - acc: 0.1457 - ETA: 48s - loss: 4.4502 - acc: 0.1542 - ETA: 47s - loss: 4.4161 - acc: 0.1554 - ETA: 46s - loss: 4.3737 - acc: 0.1618 - ETA: 46s - loss: 4.3196 - acc: 0.1744 - ETA: 45s - loss: 4.2720 - acc: 0.1813 - ETA: 45s - loss: 4.2353 - acc: 0.1817 - ETA: 44s - loss: 4.2123 - acc: 0.1869 - ETA: 44s - loss: 4.1692 - acc: 0.1942 - ETA: 43s - loss: 4.1334 - acc: 0.1977 - ETA: 43s - loss: 4.1067 - acc: 0.2011 - ETA: 42s - loss: 4.0839 - acc: 0.2033 - ETA: 42s - loss: 4.0452 - acc: 0.2074 - ETA: 41s - loss: 4.0084 - acc: 0.2104 - ETA: 41s - loss: 3.9841 - acc: 0.2153 - ETA: 40s - loss: 3.9469 - acc: 0.2200 - ETA: 40s - loss: 3.9176 - acc: 0.2216 - ETA: 40s - loss: 3.8936 - acc: 0.2240 - ETA: 39s - loss: 3.8637 - acc: 0.2311 - ETA: 39s - loss: 3.8415 - acc: 0.2343 - ETA: 39s - loss: 3.8185 - acc: 0.2382 - ETA: 39s - loss: 3.7887 - acc: 0.2438 - ETA: 38s - loss: 3.7632 - acc: 0.2491 - ETA: 38s - loss: 3.7495 - acc: 0.2509 - ETA: 37s - loss: 3.7144 - acc: 0.2568 - ETA: 37s - loss: 3.6813 - acc: 0.2617 - ETA: 37s - loss: 3.6597 - acc: 0.2656 - ETA: 37s - loss: 3.6488 - acc: 0.2653 - ETA: 36s - loss: 3.6274 - acc: 0.2675 - ETA: 36s - loss: 3.6029 - acc: 0.2711 - ETA: 36s - loss: 3.5972 - acc: 0.2700 - ETA: 35s - loss: 3.5764 - acc: 0.2727 - ETA: 35s - loss: 3.5558 - acc: 0.2746 - ETA: 35s - loss: 3.5297 - acc: 0.2816 - ETA: 35s - loss: 3.5144 - acc: 0.2855 - ETA: 34s - loss: 3.4845 - acc: 0.2907 - ETA: 34s - loss: 3.4361 - acc: 0.3000 - ETA: 33s - loss: 3.4142 - acc: 0.3021 - ETA: 33s - loss: 3.3942 - acc: 0.3047 - ETA: 33s - loss: 3.3748 - acc: 0.3073 - ETA: 33s - loss: 3.3572 - acc: 0.3092 - ETA: 32s - loss: 3.3398 - acc: 0.3117 - ETA: 32s - loss: 3.3224 - acc: 0.3135 - ETA: 32s - loss: 3.3032 - acc: 0.3171 - ETA: 31s - loss: 3.2849 - acc: 0.3206 - ETA: 31s - loss: 3.2686 - acc: 0.3228 - ETA: 31s - loss: 3.2560 - acc: 0.3238 - ETA: 31s - loss: 3.2397 - acc: 0.3277 - ETA: 30s - loss: 3.2255 - acc: 0.3292 - ETA: 30s - loss: 3.2121 - acc: 0.3312 - ETA: 30s - loss: 3.2049 - acc: 0.3314 - ETA: 30s - loss: 3.1815 - acc: 0.3368 - ETA: 29s - loss: 3.1565 - acc: 0.3426 - ETA: 29s - loss: 3.1386 - acc: 0.3461 - ETA: 29s - loss: 3.1284 - acc: 0.3456 - ETA: 29s - loss: 3.1180 - acc: 0.3456 - ETA: 28s - loss: 3.0993 - acc: 0.3484 - ETA: 28s - loss: 3.0870 - acc: 0.3500 - ETA: 28s - loss: 3.0746 - acc: 0.3521 - ETA: 28s - loss: 3.0623 - acc: 0.3532 - ETA: 27s - loss: 3.0474 - acc: 0.3542 - ETA: 27s - loss: 3.0377 - acc: 0.3552 - ETA: 27s - loss: 3.0227 - acc: 0.3566 - ETA: 27s - loss: 2.9854 - acc: 0.3640 - ETA: 26s - loss: 2.9736 - acc: 0.3653 - ETA: 26s - loss: 2.9587 - acc: 0.3686 - ETA: 26s - loss: 2.9427 - acc: 0.3723 - ETA: 26s - loss: 2.9255 - acc: 0.3760 - ETA: 26s - loss: 2.9117 - acc: 0.3767 - ETA: 25s - loss: 2.8980 - acc: 0.3797 - ETA: 25s - loss: 2.8757 - acc: 0.3815 - ETA: 24s - loss: 2.8454 - acc: 0.3859 - ETA: 24s - loss: 2.8346 - acc: 0.3883 - ETA: 24s - loss: 2.8231 - acc: 0.3897 - ETA: 24s - loss: 2.8110 - acc: 0.3907 - ETA: 23s - loss: 2.7956 - acc: 0.3930 - ETA: 23s - loss: 2.7720 - acc: 0.3970 - ETA: 23s - loss: 2.7564 - acc: 0.4004 - ETA: 22s - loss: 2.7351 - acc: 0.4029 - ETA: 22s - loss: 2.7121 - acc: 0.4066 - ETA: 21s - loss: 2.6941 - acc: 0.4085 - ETA: 21s - loss: 2.6702 - acc: 0.4115 - ETA: 21s - loss: 2.6625 - acc: 0.4126 - ETA: 20s - loss: 2.6480 - acc: 0.4155 - ETA: 20s - loss: 2.6247 - acc: 0.4206 - ETA: 19s - loss: 2.6056 - acc: 0.4237 - ETA: 19s - loss: 2.5821 - acc: 0.4281 - ETA: 18s - loss: 2.5511 - acc: 0.4333 - ETA: 18s - loss: 2.5282 - acc: 0.4386 - ETA: 18s - loss: 2.5001 - acc: 0.4434 - ETA: 17s - loss: 2.4704 - acc: 0.4486 - ETA: 17s - loss: 2.4654 - acc: 0.4493 - ETA: 16s - loss: 2.4370 - acc: 0.4540 - ETA: 16s - loss: 2.4131 - acc: 0.4578 - ETA: 15s - loss: 2.4020 - acc: 0.4594 - ETA: 15s - loss: 2.3843 - acc: 0.4618 - ETA: 15s - loss: 2.3649 - acc: 0.4657 - ETA: 14s - loss: 2.3430 - acc: 0.4691 - ETA: 14s - loss: 2.3247 - acc: 0.4735 - ETA: 13s - loss: 2.2990 - acc: 0.4774 - ETA: 13s - loss: 2.2754 - acc: 0.4808 - ETA: 12s - loss: 2.2588 - acc: 0.4829 - ETA: 12s - loss: 2.2415 - acc: 0.4854 - ETA: 12s - loss: 2.2240 - acc: 0.4876 - ETA: 11s - loss: 2.2064 - acc: 0.4902 - ETA: 11s - loss: 2.1941 - acc: 0.4928 - ETA: 10s - loss: 2.1736 - acc: 0.4963 - ETA: 10s - loss: 2.1545 - acc: 0.5013 - ETA: 10s - loss: 2.1346 - acc: 0.5053 - ETA: 9s - loss: 2.1157 - acc: 0.5095 - ETA: 9s - loss: 2.0986 - acc: 0.5118 - ETA: 9s - loss: 2.0825 - acc: 0.5148 - ETA: 8s - loss: 2.0668 - acc: 0.5172 - ETA: 8s - loss: 2.0470 - acc: 0.5204 - ETA: 8s - loss: 2.0365 - acc: 0.5221 - ETA: 8s - loss: 2.0217 - acc: 0.5245 - ETA: 7s - loss: 2.0047 - acc: 0.5266 - ETA: 7s - loss: 1.9837 - acc: 0.5310 - ETA: 7s - loss: 1.9740 - acc: 0.5331 - ETA: 6s - loss: 1.9619 - acc: 0.5353 - ETA: 6s - loss: 1.9512 - acc: 0.5362 - ETA: 6s - loss: 1.9272 - acc: 0.5404 - ETA: 5s - loss: 1.9177 - acc: 0.5432 - ETA: 5s - loss: 1.9025 - acc: 0.5457 - ETA: 5s - loss: 1.8874 - acc: 0.5488 - ETA: 5s - loss: 1.8738 - acc: 0.5516 - ETA: 4s - loss: 1.8559 - acc: 0.5552 - ETA: 4s - loss: 1.8427 - acc: 0.5569 - ETA: 4s - loss: 1.8278 - acc: 0.5598 - ETA: 3s - loss: 1.8145 - acc: 0.5622 - ETA: 3s - loss: 1.7965 - acc: 0.5667 - ETA: 3s - loss: 1.7857 - acc: 0.5689 - ETA: 3s - loss: 1.7744 - acc: 0.5710 - ETA: 2s - loss: 1.7567 - acc: 0.5748 - ETA: 2s - loss: 1.7429 - acc: 0.5771 - ETA: 2s - loss: 1.7244 - acc: 0.5808 - ETA: 1s - loss: 1.7088 - acc: 0.5844 - ETA: 1s - loss: 1.6991 - acc: 0.5862 - ETA: 1s - loss: 1.6830 - acc: 0.5891 - ETA: 0s - loss: 1.6670 - acc: 0.5920 - ETA: 0s - loss: 1.6561 - acc: 0.5939 - ETA: 0s - loss: 1.6458 - acc: 0.5953 - ETA: 0s - loss: 1.6297 - acc: 0.5988Epoch 00000: val_loss improved from inf to 0.79178, saving model to weights.best.Resnet50.hdf5 6680/6680 [==============================] - 16s - loss: 1.6266 - acc: 0.5996 - val_loss: 0.7918 - val_acc: 0.7689 Epoch 2/20 6600/6680 [============================>.] - ETA: 3s - loss: 0.4916 - acc: 0.8500 - ETA: 3s - loss: 0.5504 - acc: 0.8143 - ETA: 3s - loss: 0.4600 - acc: 0.8542 - ETA: 3s - loss: 0.4171 - acc: 0.8735 - ETA: 3s - loss: 0.4052 - acc: 0.8773 - ETA: 3s - loss: 0.3841 - acc: 0.8852 - ETA: 3s - loss: 0.3836 - acc: 0.8844 - ETA: 3s - loss: 0.3810 - acc: 0.8811 - ETA: 3s - loss: 0.3809 - acc: 0.8798 - ETA: 2s - loss: 0.3859 - acc: 0.8777 - ETA: 2s - loss: 0.3895 - acc: 0.8750 - ETA: 2s - loss: 0.3994 - acc: 0.8737 - ETA: 2s - loss: 0.4030 - acc: 0.8742 - ETA: 2s - loss: 0.4064 - acc: 0.8701 - ETA: 2s - loss: 0.4108 - acc: 0.8688 - ETA: 2s - loss: 0.4144 - acc: 0.8669 - ETA: 2s - loss: 0.4131 - acc: 0.8663 - ETA: 2s - loss: 0.4122 - acc: 0.8657 - ETA: 2s - loss: 0.4259 - acc: 0.8617 - ETA: 2s - loss: 0.4322 - acc: 0.8606 - ETA: 2s - loss: 0.4270 - acc: 0.8639 - ETA: 2s - loss: 0.4329 - acc: 0.8628 - ETA: 2s - loss: 0.4290 - acc: 0.8645 - ETA: 2s - loss: 0.4341 - acc: 0.8634 - ETA: 2s - loss: 0.4328 - acc: 0.8637 - ETA: 2s - loss: 0.4330 - acc: 0.8640 - ETA: 2s - loss: 0.4293 - acc: 0.8649 - ETA: 1s - loss: 0.4306 - acc: 0.8644 - ETA: 1s - loss: 0.4316 - acc: 0.8656 - ETA: 1s - loss: 0.4307 - acc: 0.8651 - ETA: 1s - loss: 0.4284 - acc: 0.8653 - ETA: 1s - loss: 0.4288 - acc: 0.8654 - ETA: 1s - loss: 0.4276 - acc: 0.8662 - ETA: 1s - loss: 0.4296 - acc: 0.8663 - ETA: 1s - loss: 0.4293 - acc: 0.8660 - ETA: 1s - loss: 0.4301 - acc: 0.8652 - ETA: 1s - loss: 0.4319 - acc: 0.8647 - ETA: 1s - loss: 0.4326 - acc: 0.8646 - ETA: 1s - loss: 0.4364 - acc: 0.8632 - ETA: 1s - loss: 0.4382 - acc: 0.8631 - ETA: 1s - loss: 0.4415 - acc: 0.8623 - ETA: 1s - loss: 0.4413 - acc: 0.8627 - ETA: 1s - loss: 0.4433 - acc: 0.8622 - ETA: 1s - loss: 0.4413 - acc: 0.8635 - ETA: 1s - loss: 0.4402 - acc: 0.8639 - ETA: 1s - loss: 0.4399 - acc: 0.8640 - ETA: 0s - loss: 0.4439 - acc: 0.8637 - ETA: 0s - loss: 0.4437 - acc: 0.8636 - ETA: 0s - loss: 0.4462 - acc: 0.8623 - ETA: 0s - loss: 0.4500 - acc: 0.8615 - ETA: 0s - loss: 0.4531 - acc: 0.8607 - ETA: 0s - loss: 0.4553 - acc: 0.8605 - ETA: 0s - loss: 0.4556 - acc: 0.8609 - ETA: 0s - loss: 0.4557 - acc: 0.8606 - ETA: 0s - loss: 0.4534 - acc: 0.8609 - ETA: 0s - loss: 0.4533 - acc: 0.8611 - ETA: 0s - loss: 0.4543 - acc: 0.8602 - ETA: 0s - loss: 0.4521 - acc: 0.8605 - ETA: 0s - loss: 0.4510 - acc: 0.8610 - ETA: 0s - loss: 0.4493 - acc: 0.8618 - ETA: 0s - loss: 0.4508 - acc: 0.8615 - ETA: 0s - loss: 0.4496 - acc: 0.8622 - ETA: 0s - loss: 0.4513 - acc: 0.8611 - ETA: 0s - loss: 0.4503 - acc: 0.8618 - ETA: 0s - loss: 0.4490 - acc: 0.8618Epoch 00001: val_loss improved from 0.79178 to 0.71846, saving model to weights.best.Resnet50.hdf5 6680/6680 [==============================] - 3s - loss: 0.4488 - acc: 0.8615 - val_loss: 0.7185 - val_acc: 0.7868 Epoch 3/20 6600/6680 [============================>.] - ETA: 3s - loss: 0.0401 - acc: 1.0000 - ETA: 3s - loss: 0.1818 - acc: 0.9583 - ETA: 3s - loss: 0.2350 - acc: 0.9273 - ETA: 3s - loss: 0.2162 - acc: 0.9375 - ETA: 3s - loss: 0.2038 - acc: 0.9409 - ETA: 3s - loss: 0.1937 - acc: 0.9464 - ETA: 2s - loss: 0.2025 - acc: 0.9441 - ETA: 2s - loss: 0.2118 - acc: 0.9385 - ETA: 2s - loss: 0.2247 - acc: 0.9341 - ETA: 2s - loss: 0.2152 - acc: 0.9388 - ETA: 2s - loss: 0.2203 - acc: 0.9333 - ETA: 2s - loss: 0.2185 - acc: 0.9331 - ETA: 2s - loss: 0.2272 - acc: 0.9305 - ETA: 2s - loss: 0.2390 - acc: 0.9261 - ETA: 2s - loss: 0.2410 - acc: 0.9243 - ETA: 2s - loss: 0.2481 - acc: 0.9215 - ETA: 2s - loss: 0.2507 - acc: 0.9214 - ETA: 2s - loss: 0.2508 - acc: 0.9213 - ETA: 2s - loss: 0.2504 - acc: 0.9218 - ETA: 2s - loss: 0.2449 - acc: 0.9247 - ETA: 2s - loss: 0.2508 - acc: 0.9221 - ETA: 2s - loss: 0.2520 - acc: 0.9216 - ETA: 2s - loss: 0.2496 - acc: 0.9224 - ETA: 2s - loss: 0.2486 - acc: 0.9227 - ETA: 2s - loss: 0.2456 - acc: 0.9238 - ETA: 2s - loss: 0.2430 - acc: 0.9236 - ETA: 2s - loss: 0.2419 - acc: 0.9237 - ETA: 1s - loss: 0.2368 - acc: 0.9252 - ETA: 1s - loss: 0.2348 - acc: 0.9255 - ETA: 1s - loss: 0.2367 - acc: 0.9245 - ETA: 1s - loss: 0.2374 - acc: 0.9239 - ETA: 1s - loss: 0.2421 - acc: 0.9226 - ETA: 1s - loss: 0.2418 - acc: 0.9231 - ETA: 1s - loss: 0.2408 - acc: 0.9230 - ETA: 1s - loss: 0.2413 - acc: 0.9223 - ETA: 1s - loss: 0.2437 - acc: 0.9220 - ETA: 1s - loss: 0.2424 - acc: 0.9228 - ETA: 1s - loss: 0.2438 - acc: 0.9228 - ETA: 1s - loss: 0.2426 - acc: 0.9230 - ETA: 1s - loss: 0.2446 - acc: 0.9232 - ETA: 1s - loss: 0.2431 - acc: 0.9236 - ETA: 1s - loss: 0.2470 - acc: 0.9221 - ETA: 1s - loss: 0.2503 - acc: 0.9209 - ETA: 1s - loss: 0.2519 - acc: 0.9202 - ETA: 1s - loss: 0.2549 - acc: 0.9189 - ETA: 0s - loss: 0.2544 - acc: 0.9189 - ETA: 0s - loss: 0.2540 - acc: 0.9185 - ETA: 0s - loss: 0.2550 - acc: 0.9176 - ETA: 0s - loss: 0.2561 - acc: 0.9176 - ETA: 0s - loss: 0.2568 - acc: 0.9175 - ETA: 0s - loss: 0.2568 - acc: 0.9177 - ETA: 0s - loss: 0.2585 - acc: 0.9177 - ETA: 0s - loss: 0.2607 - acc: 0.9178 - ETA: 0s - loss: 0.2633 - acc: 0.9169 - ETA: 0s - loss: 0.2639 - acc: 0.9164 - ETA: 0s - loss: 0.2642 - acc: 0.9164 - ETA: 0s - loss: 0.2639 - acc: 0.9163 - ETA: 0s - loss: 0.2646 - acc: 0.9163 - ETA: 0s - loss: 0.2643 - acc: 0.9164 - ETA: 0s - loss: 0.2654 - acc: 0.9163 - ETA: 0s - loss: 0.2653 - acc: 0.9162 - ETA: 0s - loss: 0.2663 - acc: 0.9158 - ETA: 0s - loss: 0.2646 - acc: 0.9162 - ETA: 0s - loss: 0.2647 - acc: 0.9162Epoch 00002: val_loss improved from 0.71846 to 0.63942, saving model to weights.best.Resnet50.hdf5 6680/6680 [==============================] - 3s - loss: 0.2650 - acc: 0.9160 - val_loss: 0.6394 - val_acc: 0.8168 Epoch 4/20 6660/6680 [============================>.] - ETA: 3s - loss: 0.0682 - acc: 1.0000 - ETA: 3s - loss: 0.1643 - acc: 0.9500 - ETA: 3s - loss: 0.1523 - acc: 0.9500 - ETA: 3s - loss: 0.1714 - acc: 0.9556 - ETA: 3s - loss: 0.1455 - acc: 0.9630 - ETA: 3s - loss: 0.1397 - acc: 0.9655 - ETA: 2s - loss: 0.1493 - acc: 0.9603 - ETA: 2s - loss: 0.1496 - acc: 0.9603 - ETA: 2s - loss: 0.1406 - acc: 0.9636 - ETA: 2s - loss: 0.1550 - acc: 0.9541 - ETA: 2s - loss: 0.1580 - acc: 0.9537 - ETA: 2s - loss: 0.1625 - acc: 0.9542 - ETA: 2s - loss: 0.1609 - acc: 0.9545 - ETA: 2s - loss: 0.1636 - acc: 0.9535 - ETA: 2s - loss: 0.1653 - acc: 0.9500 - ETA: 2s - loss: 0.1648 - acc: 0.9500 - ETA: 2s - loss: 0.1646 - acc: 0.9506 - ETA: 2s - loss: 0.1654 - acc: 0.9495 - ETA: 2s - loss: 0.1673 - acc: 0.9490 - ETA: 2s - loss: 0.1690 - acc: 0.9481 - ETA: 2s - loss: 0.1650 - acc: 0.9491 - ETA: 2s - loss: 0.1646 - acc: 0.9491 - ETA: 2s - loss: 0.1615 - acc: 0.9504 - ETA: 2s - loss: 0.1621 - acc: 0.9512 - ETA: 1s - loss: 0.1617 - acc: 0.9504 - ETA: 1s - loss: 0.1593 - acc: 0.9519 - ETA: 1s - loss: 0.1634 - acc: 0.9504 - ETA: 1s - loss: 0.1635 - acc: 0.9503 - ETA: 1s - loss: 0.1641 - acc: 0.9507 - ETA: 1s - loss: 0.1630 - acc: 0.9513 - ETA: 1s - loss: 0.1626 - acc: 0.9506 - ETA: 1s - loss: 0.1653 - acc: 0.9500 - ETA: 1s - loss: 0.1665 - acc: 0.9497 - ETA: 1s - loss: 0.1676 - acc: 0.9489 - ETA: 1s - loss: 0.1655 - acc: 0.9500 - ETA: 1s - loss: 0.1668 - acc: 0.9497 - ETA: 1s - loss: 0.1691 - acc: 0.9489 - ETA: 1s - loss: 0.1684 - acc: 0.9495 - ETA: 1s - loss: 0.1668 - acc: 0.9500 - ETA: 1s - loss: 0.1651 - acc: 0.9505 - ETA: 1s - loss: 0.1633 - acc: 0.9510 - ETA: 1s - loss: 0.1614 - acc: 0.9512 - ETA: 1s - loss: 0.1607 - acc: 0.9511 - ETA: 1s - loss: 0.1610 - acc: 0.9511 - ETA: 1s - loss: 0.1623 - acc: 0.9509 - ETA: 0s - loss: 0.1637 - acc: 0.9502 - ETA: 0s - loss: 0.1616 - acc: 0.9506 - ETA: 0s - loss: 0.1637 - acc: 0.9498 - ETA: 0s - loss: 0.1643 - acc: 0.9498 - ETA: 0s - loss: 0.1638 - acc: 0.9500 - ETA: 0s - loss: 0.1642 - acc: 0.9496 - ETA: 0s - loss: 0.1634 - acc: 0.9500 - ETA: 0s - loss: 0.1643 - acc: 0.9500 - ETA: 0s - loss: 0.1654 - acc: 0.9498 - ETA: 0s - loss: 0.1676 - acc: 0.9490 - ETA: 0s - loss: 0.1681 - acc: 0.9488 - ETA: 0s - loss: 0.1700 - acc: 0.9482 - ETA: 0s - loss: 0.1731 - acc: 0.9472 - ETA: 0s - loss: 0.1749 - acc: 0.9471 - ETA: 0s - loss: 0.1763 - acc: 0.9465 - ETA: 0s - loss: 0.1761 - acc: 0.9465 - ETA: 0s - loss: 0.1768 - acc: 0.9463 - ETA: 0s - loss: 0.1769 - acc: 0.9462 - ETA: 0s - loss: 0.1763 - acc: 0.9465Epoch 00003: val_loss improved from 0.63942 to 0.62551, saving model to weights.best.Resnet50.hdf5 6680/6680 [==============================] - 3s - loss: 0.1768 - acc: 0.9466 - val_loss: 0.6255 - val_acc: 0.8192 Epoch 5/20 6660/6680 [============================>.] - ETA: 3s - loss: 0.1726 - acc: 1.0000 - ETA: 3s - loss: 0.0575 - acc: 1.0000 - ETA: 3s - loss: 0.0817 - acc: 0.9909 - ETA: 3s - loss: 0.0840 - acc: 0.9906 - ETA: 3s - loss: 0.0841 - acc: 0.9905 - ETA: 3s - loss: 0.0838 - acc: 0.9846 - ETA: 3s - loss: 0.0810 - acc: 0.9855 - ETA: 3s - loss: 0.0788 - acc: 0.9861 - ETA: 2s - loss: 0.0798 - acc: 0.9845 - ETA: 2s - loss: 0.0917 - acc: 0.9787 - ETA: 2s - loss: 0.0927 - acc: 0.9774 - ETA: 2s - loss: 0.0917 - acc: 0.9763 - ETA: 2s - loss: 0.0902 - acc: 0.9754 - ETA: 2s - loss: 0.0920 - acc: 0.9739 - ETA: 2s - loss: 0.0883 - acc: 0.9753 - ETA: 2s - loss: 0.0888 - acc: 0.9747 - ETA: 2s - loss: 0.0951 - acc: 0.9713 - ETA: 2s - loss: 0.0936 - acc: 0.9721 - ETA: 2s - loss: 0.0919 - acc: 0.9728 - ETA: 2s - loss: 0.0935 - acc: 0.9722 - ETA: 2s - loss: 0.0980 - acc: 0.9712 - ETA: 2s - loss: 0.0986 - acc: 0.9703 - ETA: 2s - loss: 0.0976 - acc: 0.9702 - ETA: 2s - loss: 0.0990 - acc: 0.9694 - ETA: 1s - loss: 0.1014 - acc: 0.9683 - ETA: 1s - loss: 0.1034 - acc: 0.9680 - ETA: 1s - loss: 0.1047 - acc: 0.9674 - ETA: 1s - loss: 0.1062 - acc: 0.9675 - ETA: 1s - loss: 0.1063 - acc: 0.9675 - ETA: 1s - loss: 0.1056 - acc: 0.9679 - ETA: 1s - loss: 0.1058 - acc: 0.9677 - ETA: 1s - loss: 0.1060 - acc: 0.9675 - ETA: 1s - loss: 0.1069 - acc: 0.9675 - ETA: 1s - loss: 0.1053 - acc: 0.9676 - ETA: 1s - loss: 0.1059 - acc: 0.9669 - ETA: 1s - loss: 0.1051 - acc: 0.9669 - ETA: 1s - loss: 0.1052 - acc: 0.9670 - ETA: 1s - loss: 0.1067 - acc: 0.9666 - ETA: 1s - loss: 0.1101 - acc: 0.9654 - ETA: 1s - loss: 0.1129 - acc: 0.9648 - ETA: 1s - loss: 0.1136 - acc: 0.9644 - ETA: 1s - loss: 0.1147 - acc: 0.9641 - ETA: 1s - loss: 0.1147 - acc: 0.9637 - ETA: 1s - loss: 0.1157 - acc: 0.9632 - ETA: 1s - loss: 0.1151 - acc: 0.9634 - ETA: 0s - loss: 0.1145 - acc: 0.9635 - ETA: 0s - loss: 0.1158 - acc: 0.9632 - ETA: 0s - loss: 0.1157 - acc: 0.9636 - ETA: 0s - loss: 0.1148 - acc: 0.9640 - ETA: 0s - loss: 0.1171 - acc: 0.9633 - ETA: 0s - loss: 0.1196 - acc: 0.9628 - ETA: 0s - loss: 0.1201 - acc: 0.9622 - ETA: 0s - loss: 0.1216 - acc: 0.9616 - ETA: 0s - loss: 0.1229 - acc: 0.9610 - ETA: 0s - loss: 0.1222 - acc: 0.9611 - ETA: 0s - loss: 0.1224 - acc: 0.9614 - ETA: 0s - loss: 0.1209 - acc: 0.9618 - ETA: 0s - loss: 0.1218 - acc: 0.9617 - ETA: 0s - loss: 0.1257 - acc: 0.9604 - ETA: 0s - loss: 0.1261 - acc: 0.9605 - ETA: 0s - loss: 0.1274 - acc: 0.9608 - ETA: 0s - loss: 0.1272 - acc: 0.9608 - ETA: 0s - loss: 0.1268 - acc: 0.9608Epoch 00004: val_loss did not improve 6680/6680 [==============================] - 3s - loss: 0.1271 - acc: 0.9608 - val_loss: 0.7038 - val_acc: 0.8096 Epoch 6/20 6600/6680 [============================>.] - ETA: 3s - loss: 0.1406 - acc: 0.9500 - ETA: 3s - loss: 0.0521 - acc: 0.9833 - ETA: 3s - loss: 0.0800 - acc: 0.9773 - ETA: 3s - loss: 0.0676 - acc: 0.9781 - ETA: 3s - loss: 0.0613 - acc: 0.9810 - ETA: 3s - loss: 0.0593 - acc: 0.9827 - ETA: 3s - loss: 0.0598 - acc: 0.9806 - ETA: 3s - loss: 0.0694 - acc: 0.9792 - ETA: 2s - loss: 0.0666 - acc: 0.9793 - ETA: 2s - loss: 0.0659 - acc: 0.9793 - ETA: 2s - loss: 0.0704 - acc: 0.9784 - ETA: 2s - loss: 0.0666 - acc: 0.9804 - ETA: 2s - loss: 0.0695 - acc: 0.9795 - ETA: 2s - loss: 0.0686 - acc: 0.9803 - ETA: 2s - loss: 0.0711 - acc: 0.9796 - ETA: 2s - loss: 0.0685 - acc: 0.9809 - ETA: 2s - loss: 0.0702 - acc: 0.9796 - ETA: 2s - loss: 0.0687 - acc: 0.9802 - ETA: 2s - loss: 0.0731 - acc: 0.9786 - ETA: 2s - loss: 0.0763 - acc: 0.9781 - ETA: 2s - loss: 0.0751 - acc: 0.9782 - ETA: 2s - loss: 0.0751 - acc: 0.9778 - ETA: 2s - loss: 0.0740 - acc: 0.9779 - ETA: 2s - loss: 0.0731 - acc: 0.9776 - ETA: 2s - loss: 0.0738 - acc: 0.9773 - ETA: 2s - loss: 0.0735 - acc: 0.9778 - ETA: 2s - loss: 0.0731 - acc: 0.9784 - ETA: 1s - loss: 0.0737 - acc: 0.9786 - ETA: 1s - loss: 0.0726 - acc: 0.9792 - ETA: 1s - loss: 0.0709 - acc: 0.9797 - ETA: 1s - loss: 0.0706 - acc: 0.9798 - ETA: 1s - loss: 0.0704 - acc: 0.9796 - ETA: 1s - loss: 0.0709 - acc: 0.9795 - ETA: 1s - loss: 0.0712 - acc: 0.9796 - ETA: 1s - loss: 0.0702 - acc: 0.9803 - ETA: 1s - loss: 0.0717 - acc: 0.9798 - ETA: 1s - loss: 0.0739 - acc: 0.9786 - ETA: 1s - loss: 0.0765 - acc: 0.9783 - ETA: 1s - loss: 0.0777 - acc: 0.9777 - ETA: 1s - loss: 0.0783 - acc: 0.9774 - ETA: 1s - loss: 0.0793 - acc: 0.9770 - ETA: 1s - loss: 0.0807 - acc: 0.9761 - ETA: 1s - loss: 0.0818 - acc: 0.9760 - ETA: 1s - loss: 0.0824 - acc: 0.9759 - ETA: 0s - loss: 0.0817 - acc: 0.9762 - ETA: 0s - loss: 0.0808 - acc: 0.9765 - ETA: 0s - loss: 0.0801 - acc: 0.9767 - ETA: 0s - loss: 0.0807 - acc: 0.9764 - ETA: 0s - loss: 0.0805 - acc: 0.9763 - ETA: 0s - loss: 0.0804 - acc: 0.9765 - ETA: 0s - loss: 0.0802 - acc: 0.9766 - ETA: 0s - loss: 0.0807 - acc: 0.9761 - ETA: 0s - loss: 0.0811 - acc: 0.9760 - ETA: 0s - loss: 0.0829 - acc: 0.9757 - ETA: 0s - loss: 0.0830 - acc: 0.9758 - ETA: 0s - loss: 0.0828 - acc: 0.9757 - ETA: 0s - loss: 0.0827 - acc: 0.9758 - ETA: 0s - loss: 0.0829 - acc: 0.9755 - ETA: 0s - loss: 0.0838 - acc: 0.9749 - ETA: 0s - loss: 0.0856 - acc: 0.9747 - ETA: 0s - loss: 0.0856 - acc: 0.9746 - ETA: 0s - loss: 0.0854 - acc: 0.9745 - ETA: 0s - loss: 0.0866 - acc: 0.9743 - ETA: 0s - loss: 0.0862 - acc: 0.9745Epoch 00005: val_loss did not improve 6680/6680 [==============================] - 3s - loss: 0.0855 - acc: 0.9749 - val_loss: 0.7020 - val_acc: 0.8204 Epoch 7/20 6600/6680 [============================>.] - ETA: 3s - loss: 0.1021 - acc: 0.9000 - ETA: 3s - loss: 0.0550 - acc: 0.9750 - ETA: 3s - loss: 0.0449 - acc: 0.9818 - ETA: 3s - loss: 0.0382 - acc: 0.9875 - ETA: 3s - loss: 0.0447 - acc: 0.9881 - ETA: 3s - loss: 0.0463 - acc: 0.9846 - ETA: 3s - loss: 0.0442 - acc: 0.9855 - ETA: 3s - loss: 0.0507 - acc: 0.9833 - ETA: 3s - loss: 0.0495 - acc: 0.9829 - ETA: 2s - loss: 0.0449 - acc: 0.9851 - ETA: 2s - loss: 0.0430 - acc: 0.9856 - ETA: 2s - loss: 0.0419 - acc: 0.9862 - ETA: 2s - loss: 0.0451 - acc: 0.9852 - ETA: 2s - loss: 0.0464 - acc: 0.9850 - ETA: 2s - loss: 0.0473 - acc: 0.9849 - ETA: 2s - loss: 0.0476 - acc: 0.9854 - ETA: 2s - loss: 0.0473 - acc: 0.9852 - ETA: 2s - loss: 0.0462 - acc: 0.9856 - ETA: 2s - loss: 0.0482 - acc: 0.9845 - ETA: 2s - loss: 0.0486 - acc: 0.9844 - ETA: 2s - loss: 0.0470 - acc: 0.9853 - ETA: 2s - loss: 0.0484 - acc: 0.9847 - ETA: 2s - loss: 0.0485 - acc: 0.9847 - ETA: 1s - loss: 0.0473 - acc: 0.9854 - ETA: 1s - loss: 0.0471 - acc: 0.9860 - ETA: 1s - loss: 0.0494 - acc: 0.9849 - ETA: 1s - loss: 0.0500 - acc: 0.9845 - ETA: 1s - loss: 0.0489 - acc: 0.9851 - ETA: 1s - loss: 0.0511 - acc: 0.9840 - ETA: 1s - loss: 0.0506 - acc: 0.9841 - ETA: 1s - loss: 0.0510 - acc: 0.9837 - ETA: 1s - loss: 0.0505 - acc: 0.9839 - ETA: 1s - loss: 0.0506 - acc: 0.9838 - ETA: 1s - loss: 0.0527 - acc: 0.9840 - ETA: 1s - loss: 0.0550 - acc: 0.9832 - ETA: 1s - loss: 0.0553 - acc: 0.9828 - ETA: 1s - loss: 0.0561 - acc: 0.9825 - ETA: 1s - loss: 0.0557 - acc: 0.9824 - ETA: 1s - loss: 0.0555 - acc: 0.9826 - ETA: 1s - loss: 0.0560 - acc: 0.9826 - ETA: 1s - loss: 0.0592 - acc: 0.9816 - ETA: 1s - loss: 0.0591 - acc: 0.9813 - ETA: 1s - loss: 0.0589 - acc: 0.9815 - ETA: 0s - loss: 0.0588 - acc: 0.9815 - ETA: 0s - loss: 0.0579 - acc: 0.9819 - ETA: 0s - loss: 0.0576 - acc: 0.9818 - ETA: 0s - loss: 0.0582 - acc: 0.9816 - ETA: 0s - loss: 0.0593 - acc: 0.9810 - ETA: 0s - loss: 0.0594 - acc: 0.9810 - ETA: 0s - loss: 0.0591 - acc: 0.9809 - ETA: 0s - loss: 0.0596 - acc: 0.9809 - ETA: 0s - loss: 0.0614 - acc: 0.9802 - ETA: 0s - loss: 0.0608 - acc: 0.9805 - ETA: 0s - loss: 0.0614 - acc: 0.9805 - ETA: 0s - loss: 0.0619 - acc: 0.9807 - ETA: 0s - loss: 0.0639 - acc: 0.9797 - ETA: 0s - loss: 0.0645 - acc: 0.9792 - ETA: 0s - loss: 0.0639 - acc: 0.9793 - ETA: 0s - loss: 0.0642 - acc: 0.9794 - ETA: 0s - loss: 0.0639 - acc: 0.9794 - ETA: 0s - loss: 0.0633 - acc: 0.9797 - ETA: 0s - loss: 0.0634 - acc: 0.9797 - ETA: 0s - loss: 0.0635 - acc: 0.9795Epoch 00006: val_loss did not improve 6680/6680 [==============================] - 3s - loss: 0.0633 - acc: 0.9796 - val_loss: 0.7433 - val_acc: 0.8108 Epoch 8/20 6580/6680 [============================>.] - ETA: 18s - loss: 0.0726 - acc: 0.9500 - ETA: 5s - loss: 0.0217 - acc: 0.9929 - ETA: 3s - loss: 0.0331 - acc: 0.9923 - ETA: 3s - loss: 0.0492 - acc: 0.9868 - ETA: 3s - loss: 0.0451 - acc: 0.9880 - ETA: 3s - loss: 0.0449 - acc: 0.9871 - ETA: 3s - loss: 0.0446 - acc: 0.9865 - ETA: 2s - loss: 0.0407 - acc: 0.9884 - ETA: 2s - loss: 0.0414 - acc: 0.9878 - ETA: 2s - loss: 0.0384 - acc: 0.9882 - ETA: 2s - loss: 0.0375 - acc: 0.9877 - ETA: 2s - loss: 0.0367 - acc: 0.9888 - ETA: 2s - loss: 0.0376 - acc: 0.9877 - ETA: 2s - loss: 0.0382 - acc: 0.9880 - ETA: 2s - loss: 0.0392 - acc: 0.9876 - ETA: 2s - loss: 0.0408 - acc: 0.9863 - ETA: 2s - loss: 0.0392 - acc: 0.9871 - ETA: 2s - loss: 0.0376 - acc: 0.9879 - ETA: 2s - loss: 0.0426 - acc: 0.9876 - ETA: 2s - loss: 0.0418 - acc: 0.9882 - ETA: 2s - loss: 0.0410 - acc: 0.9887 - ETA: 1s - loss: 0.0398 - acc: 0.9891 - ETA: 1s - loss: 0.0414 - acc: 0.9880 - ETA: 1s - loss: 0.0407 - acc: 0.9884 - ETA: 1s - loss: 0.0414 - acc: 0.9881 - ETA: 1s - loss: 0.0408 - acc: 0.9885 - ETA: 1s - loss: 0.0407 - acc: 0.9883 - ETA: 1s - loss: 0.0407 - acc: 0.9883 - ETA: 1s - loss: 0.0412 - acc: 0.9881 - ETA: 1s - loss: 0.0417 - acc: 0.9881 - ETA: 1s - loss: 0.0413 - acc: 0.9885 - ETA: 1s - loss: 0.0405 - acc: 0.9888 - ETA: 1s - loss: 0.0404 - acc: 0.9888 - ETA: 1s - loss: 0.0397 - acc: 0.9889 - ETA: 1s - loss: 0.0398 - acc: 0.9886 - ETA: 1s - loss: 0.0403 - acc: 0.9884 - ETA: 1s - loss: 0.0397 - acc: 0.9887 - ETA: 1s - loss: 0.0393 - acc: 0.9890 - ETA: 1s - loss: 0.0392 - acc: 0.9890 - ETA: 1s - loss: 0.0391 - acc: 0.9893 - ETA: 1s - loss: 0.0389 - acc: 0.9893 - ETA: 1s - loss: 0.0401 - acc: 0.9891 - ETA: 1s - loss: 0.0405 - acc: 0.9886 - ETA: 0s - loss: 0.0404 - acc: 0.9887 - ETA: 0s - loss: 0.0407 - acc: 0.9885 - ETA: 0s - loss: 0.0404 - acc: 0.9887 - ETA: 0s - loss: 0.0417 - acc: 0.9886 - ETA: 0s - loss: 0.0416 - acc: 0.9886 - ETA: 0s - loss: 0.0424 - acc: 0.9880 - ETA: 0s - loss: 0.0423 - acc: 0.9881 - ETA: 0s - loss: 0.0443 - acc: 0.9874 - ETA: 0s - loss: 0.0450 - acc: 0.9869 - ETA: 0s - loss: 0.0447 - acc: 0.9869 - ETA: 0s - loss: 0.0443 - acc: 0.9871 - ETA: 0s - loss: 0.0448 - acc: 0.9870 - ETA: 0s - loss: 0.0448 - acc: 0.9869 - ETA: 0s - loss: 0.0450 - acc: 0.9870 - ETA: 0s - loss: 0.0460 - acc: 0.9868 - ETA: 0s - loss: 0.0462 - acc: 0.9869 - ETA: 0s - loss: 0.0463 - acc: 0.9869 - ETA: 0s - loss: 0.0461 - acc: 0.9870 - ETA: 0s - loss: 0.0465 - acc: 0.9869 - ETA: 0s - loss: 0.0464 - acc: 0.9869Epoch 00007: val_loss did not improve 6680/6680 [==============================] - 3s - loss: 0.0463 - acc: 0.9868 - val_loss: 0.7082 - val_acc: 0.8180 Epoch 9/20 6640/6680 [============================>.] - ETA: 3s - loss: 0.0081 - acc: 1.0000 - ETA: 2s - loss: 0.0227 - acc: 0.9929 - ETA: 2s - loss: 0.0189 - acc: 0.9962 - ETA: 2s - loss: 0.0186 - acc: 0.9974 - ETA: 2s - loss: 0.0191 - acc: 0.9980 - ETA: 2s - loss: 0.0191 - acc: 0.9984 - ETA: 2s - loss: 0.0199 - acc: 0.9973 - ETA: 2s - loss: 0.0261 - acc: 0.9953 - ETA: 2s - loss: 0.0256 - acc: 0.9959 - ETA: 2s - loss: 0.0275 - acc: 0.9936 - ETA: 2s - loss: 0.0276 - acc: 0.9934 - ETA: 2s - loss: 0.0283 - acc: 0.9925 - ETA: 2s - loss: 0.0285 - acc: 0.9925 - ETA: 2s - loss: 0.0340 - acc: 0.9905 - ETA: 2s - loss: 0.0343 - acc: 0.9905 - ETA: 2s - loss: 0.0341 - acc: 0.9900 - ETA: 2s - loss: 0.0332 - acc: 0.9906 - ETA: 2s - loss: 0.0342 - acc: 0.9907 - ETA: 2s - loss: 0.0349 - acc: 0.9903 - ETA: 1s - loss: 0.0336 - acc: 0.9908 - ETA: 1s - loss: 0.0330 - acc: 0.9908 - ETA: 1s - loss: 0.0321 - acc: 0.9913 - ETA: 1s - loss: 0.0329 - acc: 0.9913 - ETA: 1s - loss: 0.0343 - acc: 0.9909 - ETA: 1s - loss: 0.0341 - acc: 0.9909 - ETA: 1s - loss: 0.0339 - acc: 0.9909 - ETA: 1s - loss: 0.0339 - acc: 0.9908 - ETA: 1s - loss: 0.0339 - acc: 0.9908 - ETA: 1s - loss: 0.0331 - acc: 0.9911 - ETA: 1s - loss: 0.0322 - acc: 0.9914 - ETA: 1s - loss: 0.0316 - acc: 0.9917 - ETA: 1s - loss: 0.0328 - acc: 0.9911 - ETA: 1s - loss: 0.0326 - acc: 0.9910 - ETA: 1s - loss: 0.0324 - acc: 0.9910 - ETA: 1s - loss: 0.0321 - acc: 0.9910 - ETA: 1s - loss: 0.0317 - acc: 0.9912 - ETA: 1s - loss: 0.0316 - acc: 0.9912 - ETA: 1s - loss: 0.0331 - acc: 0.9912 - ETA: 1s - loss: 0.0331 - acc: 0.9914 - ETA: 1s - loss: 0.0327 - acc: 0.9916 - ETA: 1s - loss: 0.0328 - acc: 0.9913 - ETA: 0s - loss: 0.0328 - acc: 0.9913 - ETA: 0s - loss: 0.0325 - acc: 0.9914 - ETA: 0s - loss: 0.0321 - acc: 0.9915 - ETA: 0s - loss: 0.0322 - acc: 0.9915 - ETA: 0s - loss: 0.0317 - acc: 0.9917 - ETA: 0s - loss: 0.0314 - acc: 0.9918 - ETA: 0s - loss: 0.0313 - acc: 0.9918 - ETA: 0s - loss: 0.0312 - acc: 0.9918 - ETA: 0s - loss: 0.0313 - acc: 0.9917 - ETA: 0s - loss: 0.0317 - acc: 0.9915 - ETA: 0s - loss: 0.0313 - acc: 0.9917 - ETA: 0s - loss: 0.0312 - acc: 0.9916 - ETA: 0s - loss: 0.0316 - acc: 0.9914 - ETA: 0s - loss: 0.0328 - acc: 0.9911 - ETA: 0s - loss: 0.0327 - acc: 0.9912 - ETA: 0s - loss: 0.0327 - acc: 0.9912 - ETA: 0s - loss: 0.0338 - acc: 0.9907 - ETA: 0s - loss: 0.0346 - acc: 0.9904 - ETA: 0s - loss: 0.0344 - acc: 0.9904 - ETA: 0s - loss: 0.0347 - acc: 0.9901 - ETA: 0s - loss: 0.0351 - acc: 0.9899Epoch 00008: val_loss did not improve 6680/6680 [==============================] - 3s - loss: 0.0350 - acc: 0.9900 - val_loss: 0.7075 - val_acc: 0.8228 Epoch 10/20 6640/6680 [============================>.] - ETA: 3s - loss: 0.0046 - acc: 1.0000 - ETA: 3s - loss: 0.0273 - acc: 0.9929 - ETA: 3s - loss: 0.0175 - acc: 0.9958 - ETA: 3s - loss: 0.0348 - acc: 0.9912 - ETA: 3s - loss: 0.0307 - acc: 0.9909 - ETA: 3s - loss: 0.0267 - acc: 0.9926 - ETA: 3s - loss: 0.0305 - acc: 0.9906 - ETA: 3s - loss: 0.0271 - acc: 0.9919 - ETA: 2s - loss: 0.0279 - acc: 0.9917 - ETA: 2s - loss: 0.0275 - acc: 0.9927 - ETA: 2s - loss: 0.0255 - acc: 0.9935 - ETA: 2s - loss: 0.0236 - acc: 0.9942 - ETA: 2s - loss: 0.0220 - acc: 0.9947 - ETA: 2s - loss: 0.0216 - acc: 0.9951 - ETA: 2s - loss: 0.0211 - acc: 0.9955 - ETA: 2s - loss: 0.0205 - acc: 0.9958 - ETA: 2s - loss: 0.0198 - acc: 0.9961 - ETA: 2s - loss: 0.0202 - acc: 0.9958 - ETA: 2s - loss: 0.0198 - acc: 0.9961 - ETA: 2s - loss: 0.0199 - acc: 0.9958 - ETA: 2s - loss: 0.0199 - acc: 0.9952 - ETA: 1s - loss: 0.0198 - acc: 0.9954 - ETA: 1s - loss: 0.0199 - acc: 0.9952 - ETA: 1s - loss: 0.0210 - acc: 0.9951 - ETA: 1s - loss: 0.0214 - acc: 0.9949 - ETA: 1s - loss: 0.0217 - acc: 0.9948 - ETA: 1s - loss: 0.0225 - acc: 0.9943 - ETA: 1s - loss: 0.0225 - acc: 0.9942 - ETA: 1s - loss: 0.0241 - acc: 0.9941 - ETA: 1s - loss: 0.0246 - acc: 0.9940 - ETA: 1s - loss: 0.0243 - acc: 0.9943 - ETA: 1s - loss: 0.0241 - acc: 0.9942 - ETA: 1s - loss: 0.0253 - acc: 0.9938 - ETA: 1s - loss: 0.0256 - acc: 0.9935 - ETA: 1s - loss: 0.0255 - acc: 0.9934 - ETA: 1s - loss: 0.0249 - acc: 0.9936 - ETA: 1s - loss: 0.0246 - acc: 0.9938 - ETA: 1s - loss: 0.0256 - acc: 0.9933 - ETA: 1s - loss: 0.0263 - acc: 0.9928 - ETA: 0s - loss: 0.0271 - acc: 0.9925 - ETA: 0s - loss: 0.0270 - acc: 0.9925 - ETA: 0s - loss: 0.0269 - acc: 0.9925 - ETA: 0s - loss: 0.0266 - acc: 0.9925 - ETA: 0s - loss: 0.0267 - acc: 0.9924 - ETA: 0s - loss: 0.0267 - acc: 0.9924 - ETA: 0s - loss: 0.0266 - acc: 0.9925 - ETA: 0s - loss: 0.0267 - acc: 0.9927 - ETA: 0s - loss: 0.0274 - acc: 0.9924 - ETA: 0s - loss: 0.0270 - acc: 0.9926 - ETA: 0s - loss: 0.0268 - acc: 0.9927 - ETA: 0s - loss: 0.0272 - acc: 0.9927 - ETA: 0s - loss: 0.0276 - acc: 0.9926 - ETA: 0s - loss: 0.0275 - acc: 0.9927 - ETA: 0s - loss: 0.0272 - acc: 0.9929 - ETA: 0s - loss: 0.0269 - acc: 0.9930 - ETA: 0s - loss: 0.0267 - acc: 0.9931 - ETA: 0s - loss: 0.0266 - acc: 0.9932 - ETA: 0s - loss: 0.0269 - acc: 0.9932 - ETA: 0s - loss: 0.0267 - acc: 0.9933 - ETA: 0s - loss: 0.0267 - acc: 0.9931Epoch 00009: val_loss did not improve 6680/6680 [==============================] - 3s - loss: 0.0266 - acc: 0.9931 - val_loss: 0.7837 - val_acc: 0.8132 Epoch 11/20 6560/6680 [============================>.] - ETA: 3s - loss: 0.0026 - acc: 1.0000 - ETA: 3s - loss: 0.0070 - acc: 1.0000 - ETA: 3s - loss: 0.0065 - acc: 1.0000 - ETA: 3s - loss: 0.0125 - acc: 0.9971 - ETA: 3s - loss: 0.0125 - acc: 0.9955 - ETA: 3s - loss: 0.0122 - acc: 0.9944 - ETA: 3s - loss: 0.0127 - acc: 0.9937 - ETA: 2s - loss: 0.0122 - acc: 0.9946 - ETA: 2s - loss: 0.0130 - acc: 0.9940 - ETA: 2s - loss: 0.0146 - acc: 0.9936 - ETA: 2s - loss: 0.0203 - acc: 0.9933 - ETA: 2s - loss: 0.0193 - acc: 0.9939 - ETA: 2s - loss: 0.0182 - acc: 0.9944 - ETA: 2s - loss: 0.0175 - acc: 0.9948 - ETA: 2s - loss: 0.0192 - acc: 0.9944 - ETA: 2s - loss: 0.0183 - acc: 0.9948 - ETA: 2s - loss: 0.0177 - acc: 0.9951 - ETA: 2s - loss: 0.0173 - acc: 0.9954 - ETA: 2s - loss: 0.0175 - acc: 0.9957 - ETA: 2s - loss: 0.0176 - acc: 0.9954 - ETA: 2s - loss: 0.0188 - acc: 0.9946 - ETA: 2s - loss: 0.0186 - acc: 0.9949 - ETA: 2s - loss: 0.0181 - acc: 0.9951 - ETA: 2s - loss: 0.0184 - acc: 0.9953 - ETA: 2s - loss: 0.0181 - acc: 0.9955 - ETA: 2s - loss: 0.0183 - acc: 0.9953 - ETA: 2s - loss: 0.0182 - acc: 0.9955 - ETA: 2s - loss: 0.0180 - acc: 0.9956 - ETA: 1s - loss: 0.0177 - acc: 0.9958 - ETA: 1s - loss: 0.0178 - acc: 0.9956 - ETA: 1s - loss: 0.0181 - acc: 0.9954 - ETA: 1s - loss: 0.0180 - acc: 0.9955 - ETA: 1s - loss: 0.0191 - acc: 0.9954 - ETA: 1s - loss: 0.0187 - acc: 0.9955 - ETA: 1s - loss: 0.0184 - acc: 0.9956 - ETA: 1s - loss: 0.0183 - acc: 0.9958 - ETA: 1s - loss: 0.0182 - acc: 0.9959 - ETA: 1s - loss: 0.0183 - acc: 0.9958 - ETA: 1s - loss: 0.0187 - acc: 0.9954 - ETA: 1s - loss: 0.0183 - acc: 0.9955 - ETA: 1s - loss: 0.0181 - acc: 0.9957 - ETA: 1s - loss: 0.0185 - acc: 0.9953 - ETA: 1s - loss: 0.0181 - acc: 0.9955 - ETA: 1s - loss: 0.0179 - acc: 0.9956 - ETA: 1s - loss: 0.0178 - acc: 0.9957 - ETA: 0s - loss: 0.0175 - acc: 0.9958 - ETA: 0s - loss: 0.0176 - acc: 0.9957 - ETA: 0s - loss: 0.0175 - acc: 0.9958 - ETA: 0s - loss: 0.0174 - acc: 0.9959 - ETA: 0s - loss: 0.0174 - acc: 0.9958 - ETA: 0s - loss: 0.0182 - acc: 0.9955 - ETA: 0s - loss: 0.0183 - acc: 0.9956 - ETA: 0s - loss: 0.0181 - acc: 0.9957 - ETA: 0s - loss: 0.0184 - acc: 0.9956 - ETA: 0s - loss: 0.0188 - acc: 0.9955 - ETA: 0s - loss: 0.0197 - acc: 0.9955 - ETA: 0s - loss: 0.0196 - acc: 0.9954 - ETA: 0s - loss: 0.0194 - acc: 0.9955 - ETA: 0s - loss: 0.0204 - acc: 0.9953 - ETA: 0s - loss: 0.0212 - acc: 0.9950 - ETA: 0s - loss: 0.0214 - acc: 0.9948Epoch 00010: val_loss did not improve 6680/6680 [==============================] - 3s - loss: 0.0216 - acc: 0.9948 - val_loss: 0.7344 - val_acc: 0.8347 Epoch 12/20 6600/6680 [============================>.] - ETA: 3s - loss: 0.0022 - acc: 1.0000 - ETA: 2s - loss: 0.0067 - acc: 1.0000 - ETA: 2s - loss: 0.0065 - acc: 1.0000 - ETA: 2s - loss: 0.0053 - acc: 1.0000 - ETA: 2s - loss: 0.0059 - acc: 1.0000 - ETA: 2s - loss: 0.0055 - acc: 1.0000 - ETA: 2s - loss: 0.0061 - acc: 1.0000 - ETA: 2s - loss: 0.0074 - acc: 1.0000 - ETA: 2s - loss: 0.0076 - acc: 1.0000 - ETA: 2s - loss: 0.0071 - acc: 1.0000 - ETA: 2s - loss: 0.0086 - acc: 0.9992 - ETA: 2s - loss: 0.0083 - acc: 0.9992 - ETA: 2s - loss: 0.0079 - acc: 0.9993 - ETA: 2s - loss: 0.0077 - acc: 0.9994 - ETA: 2s - loss: 0.0092 - acc: 0.9988 - ETA: 2s - loss: 0.0088 - acc: 0.9989 - ETA: 2s - loss: 0.0086 - acc: 0.9989 - ETA: 2s - loss: 0.0084 - acc: 0.9990 - ETA: 2s - loss: 0.0085 - acc: 0.9986 - ETA: 2s - loss: 0.0083 - acc: 0.9986 - ETA: 2s - loss: 0.0083 - acc: 0.9987 - ETA: 2s - loss: 0.0083 - acc: 0.9987 - ETA: 1s - loss: 0.0081 - acc: 0.9988 - ETA: 1s - loss: 0.0085 - acc: 0.9985 - ETA: 1s - loss: 0.0087 - acc: 0.9981 - ETA: 1s - loss: 0.0085 - acc: 0.9982 - ETA: 1s - loss: 0.0083 - acc: 0.9983 - ETA: 1s - loss: 0.0083 - acc: 0.9983 - ETA: 1s - loss: 0.0084 - acc: 0.9984 - ETA: 1s - loss: 0.0083 - acc: 0.9984 - ETA: 1s - loss: 0.0083 - acc: 0.9985 - ETA: 1s - loss: 0.0088 - acc: 0.9982 - ETA: 1s - loss: 0.0112 - acc: 0.9977 - ETA: 1s - loss: 0.0111 - acc: 0.9978 - ETA: 1s - loss: 0.0124 - acc: 0.9976 - ETA: 1s - loss: 0.0122 - acc: 0.9976 - ETA: 1s - loss: 0.0123 - acc: 0.9977 - ETA: 1s - loss: 0.0131 - acc: 0.9975 - ETA: 1s - loss: 0.0130 - acc: 0.9976 - ETA: 1s - loss: 0.0128 - acc: 0.9976 - ETA: 1s - loss: 0.0126 - acc: 0.9977 - ETA: 1s - loss: 0.0124 - acc: 0.9977 - ETA: 1s - loss: 0.0124 - acc: 0.9978 - ETA: 1s - loss: 0.0128 - acc: 0.9974 - ETA: 0s - loss: 0.0127 - acc: 0.9974 - ETA: 0s - loss: 0.0126 - acc: 0.9975 - ETA: 0s - loss: 0.0129 - acc: 0.9973 - ETA: 0s - loss: 0.0132 - acc: 0.9972 - ETA: 0s - loss: 0.0130 - acc: 0.9973 - ETA: 0s - loss: 0.0129 - acc: 0.9973 - ETA: 0s - loss: 0.0128 - acc: 0.9974 - ETA: 0s - loss: 0.0127 - acc: 0.9974 - ETA: 0s - loss: 0.0127 - acc: 0.9975 - ETA: 0s - loss: 0.0132 - acc: 0.9970 - ETA: 0s - loss: 0.0149 - acc: 0.9965 - ETA: 0s - loss: 0.0147 - acc: 0.9966 - ETA: 0s - loss: 0.0146 - acc: 0.9966 - ETA: 0s - loss: 0.0153 - acc: 0.9965 - ETA: 0s - loss: 0.0156 - acc: 0.9964 - ETA: 0s - loss: 0.0162 - acc: 0.9963 - ETA: 0s - loss: 0.0178 - acc: 0.9959 - ETA: 0s - loss: 0.0176 - acc: 0.9959 - ETA: 0s - loss: 0.0178 - acc: 0.9958 - ETA: 0s - loss: 0.0177 - acc: 0.9959Epoch 00011: val_loss did not improve 6680/6680 [==============================] - 3s - loss: 0.0186 - acc: 0.9958 - val_loss: 0.7949 - val_acc: 0.8251 Epoch 13/20 6640/6680 [============================>.] - ETA: 3s - loss: 0.0142 - acc: 1.0000 - ETA: 3s - loss: 0.0166 - acc: 0.9917 - ETA: 3s - loss: 0.0101 - acc: 0.9955 - ETA: 3s - loss: 0.0079 - acc: 0.9969 - ETA: 3s - loss: 0.0091 - acc: 0.9952 - ETA: 3s - loss: 0.0077 - acc: 0.9962 - ETA: 3s - loss: 0.0075 - acc: 0.9968 - ETA: 3s - loss: 0.0075 - acc: 0.9972 - ETA: 2s - loss: 0.0069 - acc: 0.9976 - ETA: 2s - loss: 0.0067 - acc: 0.9978 - ETA: 2s - loss: 0.0064 - acc: 0.9980 - ETA: 2s - loss: 0.0062 - acc: 0.9982 - ETA: 2s - loss: 0.0060 - acc: 0.9984 - ETA: 2s - loss: 0.0060 - acc: 0.9986 - ETA: 2s - loss: 0.0065 - acc: 0.9987 - ETA: 2s - loss: 0.0079 - acc: 0.9975 - ETA: 2s - loss: 0.0095 - acc: 0.9971 - ETA: 2s - loss: 0.0095 - acc: 0.9968 - ETA: 2s - loss: 0.0099 - acc: 0.9965 - ETA: 2s - loss: 0.0104 - acc: 0.9962 - ETA: 2s - loss: 0.0104 - acc: 0.9964 - ETA: 2s - loss: 0.0105 - acc: 0.9961 - ETA: 2s - loss: 0.0112 - acc: 0.9959 - ETA: 1s - loss: 0.0108 - acc: 0.9961 - ETA: 1s - loss: 0.0106 - acc: 0.9963 - ETA: 1s - loss: 0.0103 - acc: 0.9964 - ETA: 1s - loss: 0.0104 - acc: 0.9966 - ETA: 1s - loss: 0.0103 - acc: 0.9967 - ETA: 1s - loss: 0.0103 - acc: 0.9968 - ETA: 1s - loss: 0.0100 - acc: 0.9970 - ETA: 1s - loss: 0.0097 - acc: 0.9971 - ETA: 1s - loss: 0.0096 - acc: 0.9972 - ETA: 1s - loss: 0.0094 - acc: 0.9973 - ETA: 1s - loss: 0.0092 - acc: 0.9973 - ETA: 1s - loss: 0.0090 - acc: 0.9974 - ETA: 1s - loss: 0.0089 - acc: 0.9975 - ETA: 1s - loss: 0.0091 - acc: 0.9973 - ETA: 1s - loss: 0.0089 - acc: 0.9974 - ETA: 1s - loss: 0.0091 - acc: 0.9972 - ETA: 1s - loss: 0.0095 - acc: 0.9971 - ETA: 0s - loss: 0.0095 - acc: 0.9972 - ETA: 0s - loss: 0.0095 - acc: 0.9972 - ETA: 0s - loss: 0.0099 - acc: 0.9971 - ETA: 0s - loss: 0.0098 - acc: 0.9972 - ETA: 0s - loss: 0.0102 - acc: 0.9970 - ETA: 0s - loss: 0.0105 - acc: 0.9967 - ETA: 0s - loss: 0.0107 - acc: 0.9966 - ETA: 0s - loss: 0.0106 - acc: 0.9967 - ETA: 0s - loss: 0.0107 - acc: 0.9968 - ETA: 0s - loss: 0.0107 - acc: 0.9967 - ETA: 0s - loss: 0.0106 - acc: 0.9967 - ETA: 0s - loss: 0.0105 - acc: 0.9968 - ETA: 0s - loss: 0.0104 - acc: 0.9969 - ETA: 0s - loss: 0.0106 - acc: 0.9968 - ETA: 0s - loss: 0.0114 - acc: 0.9967 - ETA: 0s - loss: 0.0117 - acc: 0.9966 - ETA: 0s - loss: 0.0119 - acc: 0.9965 - ETA: 0s - loss: 0.0120 - acc: 0.9964Epoch 00012: val_loss did not improve 6680/6680 [==============================] - 3s - loss: 0.0121 - acc: 0.9964 - val_loss: 0.8239 - val_acc: 0.8192 Epoch 14/20 6620/6680 [============================>.] - ETA: 3s - loss: 8.2649e-04 - acc: 1.0000 - ETA: 2s - loss: 0.0096 - acc: 0.9929 - ETA: 2s - loss: 0.0060 - acc: 0.9962 - ETA: 2s - loss: 0.0054 - acc: 0.9974 - ETA: 2s - loss: 0.0051 - acc: 0.9980 - ETA: 2s - loss: 0.0062 - acc: 0.9968 - ETA: 2s - loss: 0.0057 - acc: 0.9973 - ETA: 2s - loss: 0.0058 - acc: 0.9977 - ETA: 2s - loss: 0.0067 - acc: 0.9969 - ETA: 2s - loss: 0.0083 - acc: 0.9964 - ETA: 2s - loss: 0.0077 - acc: 0.9967 - ETA: 2s - loss: 0.0075 - acc: 0.9970 - ETA: 2s - loss: 0.0073 - acc: 0.9973 - ETA: 2s - loss: 0.0095 - acc: 0.9968 - ETA: 2s - loss: 0.0091 - acc: 0.9971 - ETA: 2s - loss: 0.0087 - acc: 0.9973 - ETA: 2s - loss: 0.0090 - acc: 0.9969 - ETA: 2s - loss: 0.0086 - acc: 0.9971 - ETA: 1s - loss: 0.0083 - acc: 0.9972 - ETA: 1s - loss: 0.0080 - acc: 0.9974 - ETA: 1s - loss: 0.0078 - acc: 0.9975 - ETA: 1s - loss: 0.0084 - acc: 0.9972 - ETA: 1s - loss: 0.0083 - acc: 0.9973 - ETA: 1s - loss: 0.0091 - acc: 0.9967 - ETA: 1s - loss: 0.0089 - acc: 0.9968 - ETA: 1s - loss: 0.0088 - acc: 0.9969 - ETA: 1s - loss: 0.0085 - acc: 0.9970 - ETA: 1s - loss: 0.0083 - acc: 0.9971 - ETA: 1s - loss: 0.0081 - acc: 0.9972 - ETA: 1s - loss: 0.0080 - acc: 0.9973 - ETA: 1s - loss: 0.0092 - acc: 0.9968 - ETA: 1s - loss: 0.0090 - acc: 0.9969 - ETA: 1s - loss: 0.0090 - acc: 0.9970 - ETA: 1s - loss: 0.0088 - acc: 0.9971 - ETA: 1s - loss: 0.0090 - acc: 0.9969 - ETA: 1s - loss: 0.0098 - acc: 0.9967 - ETA: 1s - loss: 0.0097 - acc: 0.9968 - ETA: 1s - loss: 0.0096 - acc: 0.9969 - ETA: 1s - loss: 0.0096 - acc: 0.9969 - ETA: 1s - loss: 0.0095 - acc: 0.9970 - ETA: 1s - loss: 0.0093 - acc: 0.9971 - ETA: 0s - loss: 0.0096 - acc: 0.9969 - ETA: 0s - loss: 0.0096 - acc: 0.9970 - ETA: 0s - loss: 0.0094 - acc: 0.9971 - ETA: 0s - loss: 0.0096 - acc: 0.9969 - ETA: 0s - loss: 0.0096 - acc: 0.9970 - ETA: 0s - loss: 0.0111 - acc: 0.9969 - ETA: 0s - loss: 0.0111 - acc: 0.9969 - ETA: 0s - loss: 0.0110 - acc: 0.9970 - ETA: 0s - loss: 0.0109 - acc: 0.9970 - ETA: 0s - loss: 0.0109 - acc: 0.9969 - ETA: 0s - loss: 0.0111 - acc: 0.9968 - ETA: 0s - loss: 0.0113 - acc: 0.9967 - ETA: 0s - loss: 0.0120 - acc: 0.9966 - ETA: 0s - loss: 0.0119 - acc: 0.9966 - ETA: 0s - loss: 0.0120 - acc: 0.9965 - ETA: 0s - loss: 0.0118 - acc: 0.9966 - ETA: 0s - loss: 0.0121 - acc: 0.9963 - ETA: 0s - loss: 0.0120 - acc: 0.9964 - ETA: 0s - loss: 0.0119 - acc: 0.9964 - ETA: 0s - loss: 0.0117 - acc: 0.9965 - ETA: 0s - loss: 0.0120 - acc: 0.9964Epoch 00013: val_loss did not improve 6680/6680 [==============================] - 3s - loss: 0.0119 - acc: 0.9964 - val_loss: 0.8153 - val_acc: 0.8335 Epoch 15/20 6560/6680 [============================>.] - ETA: 3s - loss: 0.0054 - acc: 1.0000 - ETA: 3s - loss: 0.0019 - acc: 1.0000 - ETA: 3s - loss: 0.0293 - acc: 0.9955 - ETA: 3s - loss: 0.0208 - acc: 0.9969 - ETA: 3s - loss: 0.0173 - acc: 0.9976 - ETA: 3s - loss: 0.0145 - acc: 0.9981 - ETA: 3s - loss: 0.0156 - acc: 0.9968 - ETA: 3s - loss: 0.0142 - acc: 0.9972 - ETA: 3s - loss: 0.0127 - acc: 0.9976 - ETA: 2s - loss: 0.0117 - acc: 0.9978 - ETA: 2s - loss: 0.0108 - acc: 0.9980 - ETA: 2s - loss: 0.0111 - acc: 0.9973 - ETA: 2s - loss: 0.0103 - acc: 0.9975 - ETA: 2s - loss: 0.0099 - acc: 0.9977 - ETA: 2s - loss: 0.0104 - acc: 0.9972 - ETA: 2s - loss: 0.0108 - acc: 0.9967 - ETA: 2s - loss: 0.0106 - acc: 0.9969 - ETA: 2s - loss: 0.0122 - acc: 0.9965 - ETA: 2s - loss: 0.0116 - acc: 0.9967 - ETA: 2s - loss: 0.0112 - acc: 0.9969 - ETA: 2s - loss: 0.0110 - acc: 0.9970 - ETA: 2s - loss: 0.0105 - acc: 0.9972 - ETA: 2s - loss: 0.0102 - acc: 0.9973 - ETA: 2s - loss: 0.0099 - acc: 0.9974 - ETA: 2s - loss: 0.0095 - acc: 0.9975 - ETA: 2s - loss: 0.0093 - acc: 0.9976 - ETA: 2s - loss: 0.0090 - acc: 0.9977 - ETA: 2s - loss: 0.0087 - acc: 0.9978 - ETA: 1s - loss: 0.0085 - acc: 0.9979 - ETA: 1s - loss: 0.0083 - acc: 0.9980 - ETA: 1s - loss: 0.0081 - acc: 0.9981 - ETA: 1s - loss: 0.0090 - acc: 0.9978 - ETA: 1s - loss: 0.0088 - acc: 0.9979 - ETA: 1s - loss: 0.0086 - acc: 0.9980 - ETA: 1s - loss: 0.0090 - acc: 0.9978 - ETA: 1s - loss: 0.0087 - acc: 0.9978 - ETA: 1s - loss: 0.0100 - acc: 0.9974 - ETA: 1s - loss: 0.0100 - acc: 0.9972 - ETA: 1s - loss: 0.0097 - acc: 0.9973 - ETA: 1s - loss: 0.0095 - acc: 0.9974 - ETA: 1s - loss: 0.0093 - acc: 0.9974 - ETA: 1s - loss: 0.0091 - acc: 0.9975 - ETA: 1s - loss: 0.0096 - acc: 0.9973 - ETA: 0s - loss: 0.0096 - acc: 0.9972 - ETA: 0s - loss: 0.0099 - acc: 0.9968 - ETA: 0s - loss: 0.0097 - acc: 0.9969 - ETA: 0s - loss: 0.0100 - acc: 0.9968 - ETA: 0s - loss: 0.0098 - acc: 0.9969 - ETA: 0s - loss: 0.0096 - acc: 0.9969 - ETA: 0s - loss: 0.0095 - acc: 0.9970 - ETA: 0s - loss: 0.0094 - acc: 0.9971 - ETA: 0s - loss: 0.0093 - acc: 0.9971 - ETA: 0s - loss: 0.0092 - acc: 0.9972 - ETA: 0s - loss: 0.0090 - acc: 0.9973 - ETA: 0s - loss: 0.0089 - acc: 0.9973 - ETA: 0s - loss: 0.0088 - acc: 0.9974 - ETA: 0s - loss: 0.0087 - acc: 0.9974 - ETA: 0s - loss: 0.0087 - acc: 0.9975 - ETA: 0s - loss: 0.0086 - acc: 0.9975 - ETA: 0s - loss: 0.0086 - acc: 0.9976Epoch 00014: val_loss did not improve 6680/6680 [==============================] - 3s - loss: 0.0087 - acc: 0.9976 - val_loss: 0.8397 - val_acc: 0.8275 Epoch 16/20 6600/6680 [============================>.] - ETA: 3s - loss: 0.0012 - acc: 1.0000 - ETA: 2s - loss: 0.0243 - acc: 0.9929 - ETA: 2s - loss: 0.0135 - acc: 0.9962 - ETA: 2s - loss: 0.0098 - acc: 0.9974 - ETA: 2s - loss: 0.0142 - acc: 0.9940 - ETA: 2s - loss: 0.0128 - acc: 0.9952 - ETA: 2s - loss: 0.0109 - acc: 0.9959 - ETA: 2s - loss: 0.0095 - acc: 0.9965 - ETA: 2s - loss: 0.0086 - acc: 0.9969 - ETA: 2s - loss: 0.0078 - acc: 0.9973 - ETA: 2s - loss: 0.0071 - acc: 0.9975 - ETA: 2s - loss: 0.0077 - acc: 0.9970 - ETA: 2s - loss: 0.0074 - acc: 0.9973 - ETA: 2s - loss: 0.0070 - acc: 0.9975 - ETA: 2s - loss: 0.0067 - acc: 0.9976 - ETA: 2s - loss: 0.0063 - acc: 0.9978 - ETA: 2s - loss: 0.0061 - acc: 0.9979 - ETA: 2s - loss: 0.0074 - acc: 0.9976 - ETA: 1s - loss: 0.0071 - acc: 0.9977 - ETA: 1s - loss: 0.0109 - acc: 0.9974 - ETA: 1s - loss: 0.0105 - acc: 0.9975 - ETA: 1s - loss: 0.0106 - acc: 0.9972 - ETA: 1s - loss: 0.0101 - acc: 0.9974 - ETA: 1s - loss: 0.0098 - acc: 0.9975 - ETA: 1s - loss: 0.0094 - acc: 0.9976 - ETA: 1s - loss: 0.0093 - acc: 0.9977 - ETA: 1s - loss: 0.0092 - acc: 0.9978 - ETA: 1s - loss: 0.0090 - acc: 0.9979 - ETA: 1s - loss: 0.0087 - acc: 0.9979 - ETA: 1s - loss: 0.0085 - acc: 0.9980 - ETA: 1s - loss: 0.0085 - acc: 0.9981 - ETA: 1s - loss: 0.0084 - acc: 0.9981 - ETA: 1s - loss: 0.0081 - acc: 0.9982 - ETA: 1s - loss: 0.0079 - acc: 0.9982 - ETA: 1s - loss: 0.0083 - acc: 0.9980 - ETA: 1s - loss: 0.0085 - acc: 0.9979 - ETA: 1s - loss: 0.0084 - acc: 0.9979 - ETA: 0s - loss: 0.0086 - acc: 0.9978 - ETA: 0s - loss: 0.0084 - acc: 0.9978 - ETA: 0s - loss: 0.0082 - acc: 0.9979 - ETA: 0s - loss: 0.0089 - acc: 0.9977 - ETA: 0s - loss: 0.0087 - acc: 0.9978 - ETA: 0s - loss: 0.0087 - acc: 0.9978 - ETA: 0s - loss: 0.0088 - acc: 0.9977 - ETA: 0s - loss: 0.0087 - acc: 0.9977 - ETA: 0s - loss: 0.0088 - acc: 0.9976 - ETA: 0s - loss: 0.0090 - acc: 0.9975 - ETA: 0s - loss: 0.0088 - acc: 0.9975 - ETA: 0s - loss: 0.0089 - acc: 0.9974 - ETA: 0s - loss: 0.0088 - acc: 0.9974 - ETA: 0s - loss: 0.0087 - acc: 0.9975 - ETA: 0s - loss: 0.0086 - acc: 0.9975 - ETA: 0s - loss: 0.0084 - acc: 0.9976 - ETA: 0s - loss: 0.0083 - acc: 0.9976 - ETA: 0s - loss: 0.0082 - acc: 0.9977 - ETA: 0s - loss: 0.0081 - acc: 0.9977Epoch 00015: val_loss did not improve 6680/6680 [==============================] - 3s - loss: 0.0086 - acc: 0.9975 - val_loss: 0.8601 - val_acc: 0.8347 Epoch 17/20 6600/6680 [============================>.] - ETA: 3s - loss: 2.1699e-04 - acc: 1.0000 - ETA: 2s - loss: 8.0555e-04 - acc: 1.0000 - ETA: 2s - loss: 8.0681e-04 - acc: 1.0000 - ETA: 2s - loss: 0.0101 - acc: 0.9974 - ETA: 2s - loss: 0.0078 - acc: 0.9980 - ETA: 2s - loss: 0.0068 - acc: 0.9984 - ETA: 2s - loss: 0.0062 - acc: 0.9986 - ETA: 2s - loss: 0.0060 - acc: 0.9988 - ETA: 2s - loss: 0.0054 - acc: 0.9989 - ETA: 2s - loss: 0.0051 - acc: 0.9990 - ETA: 2s - loss: 0.0071 - acc: 0.9982 - ETA: 2s - loss: 0.0066 - acc: 0.9984 - ETA: 2s - loss: 0.0062 - acc: 0.9985 - ETA: 2s - loss: 0.0061 - acc: 0.9986 - ETA: 2s - loss: 0.0057 - acc: 0.9987 - ETA: 2s - loss: 0.0055 - acc: 0.9988 - ETA: 2s - loss: 0.0052 - acc: 0.9988 - ETA: 2s - loss: 0.0050 - acc: 0.9989 - ETA: 2s - loss: 0.0047 - acc: 0.9990 - ETA: 2s - loss: 0.0046 - acc: 0.9990 - ETA: 2s - loss: 0.0044 - acc: 0.9991 - ETA: 2s - loss: 0.0044 - acc: 0.9991 - ETA: 2s - loss: 0.0043 - acc: 0.9991 - ETA: 2s - loss: 0.0041 - acc: 0.9992 - ETA: 2s - loss: 0.0040 - acc: 0.9992 - ETA: 1s - loss: 0.0044 - acc: 0.9989 - ETA: 1s - loss: 0.0057 - acc: 0.9985 - ETA: 1s - loss: 0.0055 - acc: 0.9986 - ETA: 1s - loss: 0.0054 - acc: 0.9986 - ETA: 1s - loss: 0.0052 - acc: 0.9987 - ETA: 1s - loss: 0.0051 - acc: 0.9987 - ETA: 1s - loss: 0.0052 - acc: 0.9985 - ETA: 1s - loss: 0.0051 - acc: 0.9985 - ETA: 1s - loss: 0.0051 - acc: 0.9985 - ETA: 1s - loss: 0.0050 - acc: 0.9986 - ETA: 1s - loss: 0.0072 - acc: 0.9984 - ETA: 1s - loss: 0.0074 - acc: 0.9981 - ETA: 1s - loss: 0.0072 - acc: 0.9982 - ETA: 1s - loss: 0.0071 - acc: 0.9982 - ETA: 1s - loss: 0.0077 - acc: 0.9980 - ETA: 1s - loss: 0.0076 - acc: 0.9981 - ETA: 1s - loss: 0.0075 - acc: 0.9981 - ETA: 1s - loss: 0.0074 - acc: 0.9982 - ETA: 1s - loss: 0.0073 - acc: 0.9982 - ETA: 1s - loss: 0.0081 - acc: 0.9980 - ETA: 1s - loss: 0.0081 - acc: 0.9981 - ETA: 0s - loss: 0.0084 - acc: 0.9979 - ETA: 0s - loss: 0.0083 - acc: 0.9979 - ETA: 0s - loss: 0.0086 - acc: 0.9976 - ETA: 0s - loss: 0.0085 - acc: 0.9976 - ETA: 0s - loss: 0.0083 - acc: 0.9977 - ETA: 0s - loss: 0.0082 - acc: 0.9977 - ETA: 0s - loss: 0.0081 - acc: 0.9978 - ETA: 0s - loss: 0.0081 - acc: 0.9978 - ETA: 0s - loss: 0.0080 - acc: 0.9978 - ETA: 0s - loss: 0.0079 - acc: 0.9979 - ETA: 0s - loss: 0.0078 - acc: 0.9979 - ETA: 0s - loss: 0.0078 - acc: 0.9980 - ETA: 0s - loss: 0.0077 - acc: 0.9980 - ETA: 0s - loss: 0.0076 - acc: 0.9980 - ETA: 0s - loss: 0.0075 - acc: 0.9981 - ETA: 0s - loss: 0.0075 - acc: 0.9981 - ETA: 0s - loss: 0.0074 - acc: 0.9981 - ETA: 0s - loss: 0.0073 - acc: 0.9982 - ETA: 0s - loss: 0.0073 - acc: 0.9982Epoch 00016: val_loss did not improve 6680/6680 [==============================] - 3s - loss: 0.0072 - acc: 0.9982 - val_loss: 0.9362 - val_acc: 0.8275 Epoch 18/20 6620/6680 [============================>.] - ETA: 3s - loss: 0.0177 - acc: 1.0000 - ETA: 3s - loss: 0.0091 - acc: 1.0000 - ETA: 3s - loss: 0.0054 - acc: 1.0000 - ETA: 3s - loss: 0.0040 - acc: 1.0000 - ETA: 3s - loss: 0.0145 - acc: 0.9977 - ETA: 3s - loss: 0.0119 - acc: 0.9981 - ETA: 3s - loss: 0.0102 - acc: 0.9984 - ETA: 3s - loss: 0.0088 - acc: 0.9986 - ETA: 2s - loss: 0.0079 - acc: 0.9988 - ETA: 2s - loss: 0.0072 - acc: 0.9989 - ETA: 2s - loss: 0.0086 - acc: 0.9981 - ETA: 2s - loss: 0.0080 - acc: 0.9982 - ETA: 2s - loss: 0.0074 - acc: 0.9984 - ETA: 2s - loss: 0.0069 - acc: 0.9985 - ETA: 2s - loss: 0.0065 - acc: 0.9986 - ETA: 2s - loss: 0.0065 - acc: 0.9987 - ETA: 2s - loss: 0.0062 - acc: 0.9988 - ETA: 2s - loss: 0.0078 - acc: 0.9983 - ETA: 2s - loss: 0.0086 - acc: 0.9978 - ETA: 2s - loss: 0.0082 - acc: 0.9980 - ETA: 2s - loss: 0.0079 - acc: 0.9981 - ETA: 2s - loss: 0.0077 - acc: 0.9982 - ETA: 2s - loss: 0.0078 - acc: 0.9978 - ETA: 2s - loss: 0.0076 - acc: 0.9979 - ETA: 2s - loss: 0.0073 - acc: 0.9980 - ETA: 1s - loss: 0.0070 - acc: 0.9981 - ETA: 1s - loss: 0.0069 - acc: 0.9982 - ETA: 1s - loss: 0.0084 - acc: 0.9979 - ETA: 1s - loss: 0.0082 - acc: 0.9980 - ETA: 1s - loss: 0.0079 - acc: 0.9981 - ETA: 1s - loss: 0.0083 - acc: 0.9979 - ETA: 1s - loss: 0.0081 - acc: 0.9979 - ETA: 1s - loss: 0.0078 - acc: 0.9980 - ETA: 1s - loss: 0.0077 - acc: 0.9981 - ETA: 1s - loss: 0.0076 - acc: 0.9981 - ETA: 1s - loss: 0.0074 - acc: 0.9982 - ETA: 1s - loss: 0.0072 - acc: 0.9982 - ETA: 1s - loss: 0.0071 - acc: 0.9983 - ETA: 1s - loss: 0.0070 - acc: 0.9983 - ETA: 1s - loss: 0.0069 - acc: 0.9984 - ETA: 1s - loss: 0.0067 - acc: 0.9984 - ETA: 0s - loss: 0.0075 - acc: 0.9983 - ETA: 0s - loss: 0.0073 - acc: 0.9983 - ETA: 0s - loss: 0.0072 - acc: 0.9983 - ETA: 0s - loss: 0.0071 - acc: 0.9984 - ETA: 0s - loss: 0.0071 - acc: 0.9984 - ETA: 0s - loss: 0.0069 - acc: 0.9985 - ETA: 0s - loss: 0.0068 - acc: 0.9985 - ETA: 0s - loss: 0.0067 - acc: 0.9985 - ETA: 0s - loss: 0.0066 - acc: 0.9986 - ETA: 0s - loss: 0.0064 - acc: 0.9986 - ETA: 0s - loss: 0.0063 - acc: 0.9986 - ETA: 0s - loss: 0.0063 - acc: 0.9986 - ETA: 0s - loss: 0.0062 - acc: 0.9987 - ETA: 0s - loss: 0.0061 - acc: 0.9987 - ETA: 0s - loss: 0.0060 - acc: 0.9987 - ETA: 0s - loss: 0.0060 - acc: 0.9987 - ETA: 0s - loss: 0.0060 - acc: 0.9988 - ETA: 0s - loss: 0.0059 - acc: 0.9988Epoch 00017: val_loss did not improve 6680/6680 [==============================] - 3s - loss: 0.0058 - acc: 0.9988 - val_loss: 0.8732 - val_acc: 0.8228 Epoch 19/20 6620/6680 [============================>.] - ETA: 3s - loss: 1.1440e-04 - acc: 1.0000 - ETA: 2s - loss: 4.2132e-04 - acc: 1.0000 - ETA: 2s - loss: 4.2250e-04 - acc: 1.0000 - ETA: 2s - loss: 4.2764e-04 - acc: 1.0000 - ETA: 2s - loss: 4.1593e-04 - acc: 1.0000 - ETA: 2s - loss: 4.1321e-04 - acc: 1.0000 - ETA: 2s - loss: 3.7705e-04 - acc: 1.0000 - ETA: 2s - loss: 3.6876e-04 - acc: 1.0000 - ETA: 2s - loss: 3.6417e-04 - acc: 1.0000 - ETA: 2s - loss: 4.5136e-04 - acc: 1.0000 - ETA: 2s - loss: 4.6121e-04 - acc: 1.0000 - ETA: 2s - loss: 4.8916e-04 - acc: 1.0000 - ETA: 2s - loss: 4.8452e-04 - acc: 1.0000 - ETA: 2s - loss: 5.1069e-04 - acc: 1.0000 - ETA: 2s - loss: 5.5216e-04 - acc: 1.0000 - ETA: 2s - loss: 5.8183e-04 - acc: 1.0000 - ETA: 2s - loss: 6.3211e-04 - acc: 1.0000 - ETA: 2s - loss: 6.3338e-04 - acc: 1.0000 - ETA: 2s - loss: 6.8819e-04 - acc: 1.0000 - ETA: 1s - loss: 0.0024 - acc: 0.9996 - ETA: 1s - loss: 0.0024 - acc: 0.9996 - ETA: 1s - loss: 0.0023 - acc: 0.9996 - ETA: 1s - loss: 0.0028 - acc: 0.9992 - ETA: 1s - loss: 0.0027 - acc: 0.9993 - ETA: 1s - loss: 0.0030 - acc: 0.9990 - ETA: 1s - loss: 0.0030 - acc: 0.9990 - ETA: 1s - loss: 0.0035 - acc: 0.9984 - ETA: 1s - loss: 0.0034 - acc: 0.9985 - ETA: 1s - loss: 0.0038 - acc: 0.9982 - ETA: 1s - loss: 0.0038 - acc: 0.9983 - ETA: 1s - loss: 0.0037 - acc: 0.9983 - ETA: 1s - loss: 0.0037 - acc: 0.9984 - ETA: 1s - loss: 0.0036 - acc: 0.9984 - ETA: 1s - loss: 0.0035 - acc: 0.9985 - ETA: 1s - loss: 0.0034 - acc: 0.9985 - ETA: 1s - loss: 0.0034 - acc: 0.9986 - ETA: 1s - loss: 0.0033 - acc: 0.9986 - ETA: 0s - loss: 0.0033 - acc: 0.9987 - ETA: 0s - loss: 0.0032 - acc: 0.9987 - ETA: 0s - loss: 0.0033 - acc: 0.9985 - ETA: 0s - loss: 0.0038 - acc: 0.9983 - ETA: 0s - loss: 0.0037 - acc: 0.9984 - ETA: 0s - loss: 0.0037 - acc: 0.9984 - ETA: 0s - loss: 0.0036 - acc: 0.9985 - ETA: 0s - loss: 0.0060 - acc: 0.9981 - ETA: 0s - loss: 0.0059 - acc: 0.9982 - ETA: 0s - loss: 0.0058 - acc: 0.9982 - ETA: 0s - loss: 0.0057 - acc: 0.9982 - ETA: 0s - loss: 0.0056 - acc: 0.9983 - ETA: 0s - loss: 0.0055 - acc: 0.9983 - ETA: 0s - loss: 0.0055 - acc: 0.9983 - ETA: 0s - loss: 0.0054 - acc: 0.9984 - ETA: 0s - loss: 0.0053 - acc: 0.9984 - ETA: 0s - loss: 0.0053 - acc: 0.9984 - ETA: 0s - loss: 0.0053 - acc: 0.9985 - ETA: 0s - loss: 0.0056 - acc: 0.9983Epoch 00018: val_loss did not improve 6680/6680 [==============================] - 3s - loss: 0.0056 - acc: 0.9984 - val_loss: 0.8781 - val_acc: 0.8275 Epoch 20/20 6620/6680 [============================>.] - ETA: 3s - loss: 0.0011 - acc: 1.0000 - ETA: 2s - loss: 0.0025 - acc: 1.0000 - ETA: 2s - loss: 0.0015 - acc: 1.0000 - ETA: 2s - loss: 0.0014 - acc: 1.0000 - ETA: 2s - loss: 0.0011 - acc: 1.0000 - ETA: 2s - loss: 9.4173e-04 - acc: 1.0000 - ETA: 2s - loss: 0.0043 - acc: 0.9986 - ETA: 2s - loss: 0.0038 - acc: 0.9988 - ETA: 2s - loss: 0.0034 - acc: 0.9990 - ETA: 2s - loss: 0.0031 - acc: 0.9991 - ETA: 2s - loss: 0.0085 - acc: 0.9984 - ETA: 2s - loss: 0.0078 - acc: 0.9985 - ETA: 2s - loss: 0.0074 - acc: 0.9986 - ETA: 2s - loss: 0.0069 - acc: 0.9987 - ETA: 2s - loss: 0.0064 - acc: 0.9988 - ETA: 2s - loss: 0.0060 - acc: 0.9989 - ETA: 2s - loss: 0.0056 - acc: 0.9990 - ETA: 2s - loss: 0.0054 - acc: 0.9990 - ETA: 1s - loss: 0.0051 - acc: 0.9991 - ETA: 1s - loss: 0.0057 - acc: 0.9987 - ETA: 1s - loss: 0.0054 - acc: 0.9988 - ETA: 1s - loss: 0.0054 - acc: 0.9988 - ETA: 1s - loss: 0.0052 - acc: 0.9989 - ETA: 1s - loss: 0.0050 - acc: 0.9989 - ETA: 1s - loss: 0.0049 - acc: 0.9990 - ETA: 1s - loss: 0.0047 - acc: 0.9990 - ETA: 1s - loss: 0.0046 - acc: 0.9990 - ETA: 1s - loss: 0.0044 - acc: 0.9991 - ETA: 1s - loss: 0.0060 - acc: 0.9988 - ETA: 1s - loss: 0.0074 - acc: 0.9983 - ETA: 1s - loss: 0.0072 - acc: 0.9983 - ETA: 1s - loss: 0.0069 - acc: 0.9984 - ETA: 1s - loss: 0.0075 - acc: 0.9982 - ETA: 1s - loss: 0.0073 - acc: 0.9982 - ETA: 1s - loss: 0.0072 - acc: 0.9983 - ETA: 1s - loss: 0.0070 - acc: 0.9983 - ETA: 1s - loss: 0.0068 - acc: 0.9984 - ETA: 0s - loss: 0.0071 - acc: 0.9982 - ETA: 0s - loss: 0.0070 - acc: 0.9983 - ETA: 0s - loss: 0.0068 - acc: 0.9983 - ETA: 0s - loss: 0.0066 - acc: 0.9983 - ETA: 0s - loss: 0.0068 - acc: 0.9982 - ETA: 0s - loss: 0.0077 - acc: 0.9978 - ETA: 0s - loss: 0.0075 - acc: 0.9979 - ETA: 0s - loss: 0.0074 - acc: 0.9979 - ETA: 0s - loss: 0.0072 - acc: 0.9980 - ETA: 0s - loss: 0.0071 - acc: 0.9980 - ETA: 0s - loss: 0.0069 - acc: 0.9981 - ETA: 0s - loss: 0.0068 - acc: 0.9981 - ETA: 0s - loss: 0.0067 - acc: 0.9981 - ETA: 0s - loss: 0.0066 - acc: 0.9982 - ETA: 0s - loss: 0.0065 - acc: 0.9982 - ETA: 0s - loss: 0.0064 - acc: 0.9982 - ETA: 0s - loss: 0.0063 - acc: 0.9983 - ETA: 0s - loss: 0.0066 - acc: 0.9982 - ETA: 0s - loss: 0.0065 - acc: 0.9982Epoch 00019: val_loss did not improve 6680/6680 [==============================] - 3s - loss: 0.0064 - acc: 0.9982 - val_loss: 0.9351 - val_acc: 0.8335 ---I am done saving model valid_Resnet50 ----
### TODO: Train the model.
checkpointer_InceptionV3 = ModelCheckpoint(filepath='weights.best.InceptionV3.hdf5',
verbose=1, save_best_only=True)
InceptionV3_model.fit(train_InceptionV3, train_targets,
validation_data=(valid_InceptionV3, valid_targets),
epochs=20, batch_size=20, callbacks=[checkpointer_InceptionV3], verbose=1)
print('---I am done saving model valid_InceptionV3 ----')
Train on 6680 samples, validate on 835 samples Epoch 1/20 6660/6680 [============================>.] - ETA: 667s - loss: 5.0949 - acc: 0.0000e+00 - ETA: 485s - loss: 6.2580 - acc: 0.0250 - ETA: 382s - loss: 6.1590 - acc: 0.0167 - ETA: 330s - loss: 6.2435 - acc: 0.0125 - ETA: 279s - loss: 6.0845 - acc: 0.0200 - ETA: 241s - loss: 5.9221 - acc: 0.0333 - ETA: 210s - loss: 5.8357 - acc: 0.0357 - ETA: 186s - loss: 5.7328 - acc: 0.0625 - ETA: 170s - loss: 5.6272 - acc: 0.0722 - ETA: 155s - loss: 5.4601 - acc: 0.0900 - ETA: 144s - loss: 5.3829 - acc: 0.1045 - ETA: 134s - loss: 5.2416 - acc: 0.1250 - ETA: 125s - loss: 5.1214 - acc: 0.1346 - ETA: 119s - loss: 5.0546 - acc: 0.1393 - ETA: 113s - loss: 5.0031 - acc: 0.1467 - ETA: 108s - loss: 4.9075 - acc: 0.1656 - ETA: 103s - loss: 4.8837 - acc: 0.1706 - ETA: 100s - loss: 4.7967 - acc: 0.1833 - ETA: 97s - loss: 4.7396 - acc: 0.1921 - ETA: 94s - loss: 4.6796 - acc: 0.1950 - ETA: 92s - loss: 4.5828 - acc: 0.2048 - ETA: 88s - loss: 4.4852 - acc: 0.2227 - ETA: 85s - loss: 4.4295 - acc: 0.2304 - ETA: 83s - loss: 4.3600 - acc: 0.2458 - ETA: 81s - loss: 4.3151 - acc: 0.2520 - ETA: 78s - loss: 4.2523 - acc: 0.2596 - ETA: 76s - loss: 4.1595 - acc: 0.2741 - ETA: 74s - loss: 4.1082 - acc: 0.2786 - ETA: 72s - loss: 4.0460 - acc: 0.2845 - ETA: 70s - loss: 4.0030 - acc: 0.2900 - ETA: 69s - loss: 3.9498 - acc: 0.2952 - ETA: 68s - loss: 3.8972 - acc: 0.2984 - ETA: 66s - loss: 3.8453 - acc: 0.3076 - ETA: 65s - loss: 3.7864 - acc: 0.3162 - ETA: 64s - loss: 3.7340 - acc: 0.3200 - ETA: 62s - loss: 3.6760 - acc: 0.3292 - ETA: 61s - loss: 3.6246 - acc: 0.3351 - ETA: 60s - loss: 3.5750 - acc: 0.3382 - ETA: 59s - loss: 3.5414 - acc: 0.3397 - ETA: 58s - loss: 3.5080 - acc: 0.3425 - ETA: 57s - loss: 3.4688 - acc: 0.3500 - ETA: 56s - loss: 3.4178 - acc: 0.3583 - ETA: 55s - loss: 3.3757 - acc: 0.3628 - ETA: 54s - loss: 3.3275 - acc: 0.3682 - ETA: 53s - loss: 3.2955 - acc: 0.3711 - ETA: 52s - loss: 3.2575 - acc: 0.3750 - ETA: 52s - loss: 3.2243 - acc: 0.3809 - ETA: 51s - loss: 3.1888 - acc: 0.3865 - ETA: 50s - loss: 3.1607 - acc: 0.3929 - ETA: 49s - loss: 3.1230 - acc: 0.3970 - ETA: 49s - loss: 3.0855 - acc: 0.4020 - ETA: 48s - loss: 3.0737 - acc: 0.4019 - ETA: 47s - loss: 3.0535 - acc: 0.4038 - ETA: 47s - loss: 3.0246 - acc: 0.4065 - ETA: 46s - loss: 2.9849 - acc: 0.4136 - ETA: 46s - loss: 2.9521 - acc: 0.4196 - ETA: 45s - loss: 2.9252 - acc: 0.4211 - ETA: 44s - loss: 2.9015 - acc: 0.4233 - ETA: 44s - loss: 2.8707 - acc: 0.4263 - ETA: 43s - loss: 2.8451 - acc: 0.4300 - ETA: 43s - loss: 2.8086 - acc: 0.4377 - ETA: 43s - loss: 2.7797 - acc: 0.4411 - ETA: 42s - loss: 2.7570 - acc: 0.4452 - ETA: 42s - loss: 2.7310 - acc: 0.4492 - ETA: 41s - loss: 2.7121 - acc: 0.4515 - ETA: 41s - loss: 2.6853 - acc: 0.4568 - ETA: 41s - loss: 2.6609 - acc: 0.4597 - ETA: 40s - loss: 2.6412 - acc: 0.4640 - ETA: 40s - loss: 2.6257 - acc: 0.4659 - ETA: 40s - loss: 2.6082 - acc: 0.4693 - ETA: 39s - loss: 2.5971 - acc: 0.4704 - ETA: 39s - loss: 2.5793 - acc: 0.4743 - ETA: 39s - loss: 2.5646 - acc: 0.4740 - ETA: 38s - loss: 2.5408 - acc: 0.4791 - ETA: 39s - loss: 2.5159 - acc: 0.4833 - ETA: 38s - loss: 2.5002 - acc: 0.4849 - ETA: 38s - loss: 2.4748 - acc: 0.4896 - ETA: 37s - loss: 2.4612 - acc: 0.4910 - ETA: 37s - loss: 2.4269 - acc: 0.4981 - ETA: 36s - loss: 2.4071 - acc: 0.5019 - ETA: 36s - loss: 2.3897 - acc: 0.5030 - ETA: 36s - loss: 2.3763 - acc: 0.5054 - ETA: 35s - loss: 2.3626 - acc: 0.5071 - ETA: 35s - loss: 2.3486 - acc: 0.5094 - ETA: 35s - loss: 2.3276 - acc: 0.5134 - ETA: 35s - loss: 2.3190 - acc: 0.5144 - ETA: 34s - loss: 2.3054 - acc: 0.5170 - ETA: 34s - loss: 2.2974 - acc: 0.5169 - ETA: 34s - loss: 2.2815 - acc: 0.5194 - ETA: 34s - loss: 2.2676 - acc: 0.5214 - ETA: 33s - loss: 2.2491 - acc: 0.5250 - ETA: 33s - loss: 2.2378 - acc: 0.5269 - ETA: 33s - loss: 2.2240 - acc: 0.5282 - ETA: 33s - loss: 2.2080 - acc: 0.5316 - ETA: 32s - loss: 2.1944 - acc: 0.5344 - ETA: 32s - loss: 2.1801 - acc: 0.5361 - ETA: 32s - loss: 2.1690 - acc: 0.5378 - ETA: 31s - loss: 2.1582 - acc: 0.5389 - ETA: 31s - loss: 2.1466 - acc: 0.5405 - ETA: 31s - loss: 2.1365 - acc: 0.5411 - ETA: 31s - loss: 2.1238 - acc: 0.5431 - ETA: 31s - loss: 2.1164 - acc: 0.5437 - ETA: 31s - loss: 2.1079 - acc: 0.5442 - ETA: 31s - loss: 2.0996 - acc: 0.5443 - ETA: 31s - loss: 2.0894 - acc: 0.5458 - ETA: 30s - loss: 2.0793 - acc: 0.5477 - ETA: 30s - loss: 2.0686 - acc: 0.5491 - ETA: 30s - loss: 2.0562 - acc: 0.5514 - ETA: 30s - loss: 2.0465 - acc: 0.5532 - ETA: 30s - loss: 2.0331 - acc: 0.5563 - ETA: 29s - loss: 2.0214 - acc: 0.5589 - ETA: 29s - loss: 2.0105 - acc: 0.5611 - ETA: 29s - loss: 2.0080 - acc: 0.5610 - ETA: 29s - loss: 1.9990 - acc: 0.5622 - ETA: 29s - loss: 1.9879 - acc: 0.5642 - ETA: 29s - loss: 1.9776 - acc: 0.5654 - ETA: 28s - loss: 1.9683 - acc: 0.5657 - ETA: 28s - loss: 1.9592 - acc: 0.5660 - ETA: 28s - loss: 1.9519 - acc: 0.5671 - ETA: 28s - loss: 1.9419 - acc: 0.5686 - ETA: 28s - loss: 1.9304 - acc: 0.5705 - ETA: 28s - loss: 1.9217 - acc: 0.5720 - ETA: 27s - loss: 1.9123 - acc: 0.5738 - ETA: 27s - loss: 1.9002 - acc: 0.5768 - ETA: 27s - loss: 1.8933 - acc: 0.5778 - ETA: 27s - loss: 1.8817 - acc: 0.5799 - ETA: 27s - loss: 1.8746 - acc: 0.5805 - ETA: 26s - loss: 1.8680 - acc: 0.5814 - ETA: 26s - loss: 1.8586 - acc: 0.5827 - ETA: 26s - loss: 1.8496 - acc: 0.5844 - ETA: 26s - loss: 1.8437 - acc: 0.5852 - ETA: 26s - loss: 1.8377 - acc: 0.5861 - ETA: 25s - loss: 1.8283 - acc: 0.5873 - ETA: 25s - loss: 1.8195 - acc: 0.5885 - ETA: 25s - loss: 1.8098 - acc: 0.5897 - ETA: 25s - loss: 1.8019 - acc: 0.5916 - ETA: 25s - loss: 1.7958 - acc: 0.5913 - ETA: 25s - loss: 1.7897 - acc: 0.5928 - ETA: 25s - loss: 1.7866 - acc: 0.5936 - ETA: 24s - loss: 1.7794 - acc: 0.5947 - ETA: 24s - loss: 1.7711 - acc: 0.5961 - ETA: 24s - loss: 1.7680 - acc: 0.5962 - ETA: 24s - loss: 1.7614 - acc: 0.5976 - ETA: 24s - loss: 1.7521 - acc: 0.5993 - ETA: 24s - loss: 1.7447 - acc: 0.6003 - ETA: 23s - loss: 1.7363 - acc: 0.6024 - ETA: 23s - loss: 1.7261 - acc: 0.6044 - ETA: 23s - loss: 1.7201 - acc: 0.6060 - ETA: 23s - loss: 1.7162 - acc: 0.6070 - ETA: 23s - loss: 1.7076 - acc: 0.6086 - ETA: 23s - loss: 1.7032 - acc: 0.6095 - ETA: 22s - loss: 1.7024 - acc: 0.6098 - ETA: 22s - loss: 1.6978 - acc: 0.6104 - ETA: 22s - loss: 1.6902 - acc: 0.6119 - ETA: 22s - loss: 1.6870 - acc: 0.6128 - ETA: 22s - loss: 1.6801 - acc: 0.6140 - ETA: 21s - loss: 1.6722 - acc: 0.6155 - ETA: 21s - loss: 1.6664 - acc: 0.6167 - ETA: 21s - loss: 1.6637 - acc: 0.6175 - ETA: 21s - loss: 1.6603 - acc: 0.6171 - ETA: 21s - loss: 1.6534 - acc: 0.6191 - ETA: 21s - loss: 1.6532 - acc: 0.6193 - ETA: 21s - loss: 1.6468 - acc: 0.6204 - ETA: 21s - loss: 1.6390 - acc: 0.6218 - ETA: 20s - loss: 1.6327 - acc: 0.6229 - ETA: 20s - loss: 1.6268 - acc: 0.6240 - ETA: 20s - loss: 1.6228 - acc: 0.6241 - ETA: 20s - loss: 1.6179 - acc: 0.6251 - ETA: 20s - loss: 1.6122 - acc: 0.6265 - ETA: 19s - loss: 1.6011 - acc: 0.6282 - ETA: 19s - loss: 1.5952 - acc: 0.6295 - ETA: 19s - loss: 1.5911 - acc: 0.6302 - ETA: 19s - loss: 1.5855 - acc: 0.6311 - ETA: 19s - loss: 1.5747 - acc: 0.6331 - ETA: 19s - loss: 1.5685 - acc: 0.6340 - ETA: 19s - loss: 1.5640 - acc: 0.6352 - ETA: 19s - loss: 1.5596 - acc: 0.6358 - ETA: 19s - loss: 1.5566 - acc: 0.6365 - ETA: 19s - loss: 1.5530 - acc: 0.6365 - ETA: 19s - loss: 1.5496 - acc: 0.6369 - ETA: 19s - loss: 1.5446 - acc: 0.6380 - ETA: 19s - loss: 1.5397 - acc: 0.6384 - ETA: 19s - loss: 1.5344 - acc: 0.6395 - ETA: 19s - loss: 1.5304 - acc: 0.6404 - ETA: 19s - loss: 1.5247 - acc: 0.6418 - ETA: 19s - loss: 1.5198 - acc: 0.6426 - ETA: 19s - loss: 1.5141 - acc: 0.6437 - ETA: 19s - loss: 1.5114 - acc: 0.6445 - ETA: 19s - loss: 1.5086 - acc: 0.6453 - ETA: 19s - loss: 1.5071 - acc: 0.6453 - ETA: 19s - loss: 1.5019 - acc: 0.6459 - ETA: 18s - loss: 1.4963 - acc: 0.6469 - ETA: 18s - loss: 1.4921 - acc: 0.6474 - ETA: 18s - loss: 1.4873 - acc: 0.6487 - ETA: 18s - loss: 1.4831 - acc: 0.6497 - ETA: 18s - loss: 1.4801 - acc: 0.6503 - ETA: 18s - loss: 1.4741 - acc: 0.6515 - 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acc: 0.6918 - ETA: 7s - loss: 1.2631 - acc: 0.6921 - ETA: 7s - loss: 1.2597 - acc: 0.6927 - ETA: 7s - loss: 1.2578 - acc: 0.6930 - ETA: 7s - loss: 1.2546 - acc: 0.6936 - ETA: 6s - loss: 1.2534 - acc: 0.6936 - ETA: 6s - loss: 1.2509 - acc: 0.6944 - ETA: 6s - loss: 1.2474 - acc: 0.6951 - ETA: 6s - loss: 1.2471 - acc: 0.6948 - ETA: 6s - loss: 1.2449 - acc: 0.6951 - ETA: 6s - loss: 1.2435 - acc: 0.6957 - ETA: 6s - loss: 1.2415 - acc: 0.6960 - ETA: 6s - loss: 1.2388 - acc: 0.6966 - ETA: 5s - loss: 1.2375 - acc: 0.6967 - ETA: 5s - loss: 1.2359 - acc: 0.6971 - ETA: 5s - loss: 1.2375 - acc: 0.6964 - ETA: 5s - loss: 1.2347 - acc: 0.6969 - ETA: 5s - loss: 1.2332 - acc: 0.6975 - ETA: 5s - loss: 1.2332 - acc: 0.6978 - ETA: 5s - loss: 1.2324 - acc: 0.6980 - ETA: 4s - loss: 1.2301 - acc: 0.6985 - ETA: 4s - loss: 1.2287 - acc: 0.6985 - ETA: 4s - loss: 1.2259 - acc: 0.6992 - ETA: 4s - loss: 1.2227 - acc: 0.7000 - ETA: 4s - loss: 1.2210 - acc: 0.7002 - ETA: 4s - loss: 1.2212 - acc: 0.6998 - ETA: 4s - loss: 1.2206 - acc: 0.6997 - ETA: 3s - loss: 1.2207 - acc: 0.6998 - ETA: 3s - loss: 1.2188 - acc: 0.7003 - ETA: 3s - loss: 1.2158 - acc: 0.7011 - ETA: 3s - loss: 1.2146 - acc: 0.7010 - ETA: 3s - loss: 1.2135 - acc: 0.7008 - ETA: 3s - loss: 1.2110 - acc: 0.7015 - ETA: 3s - loss: 1.2098 - acc: 0.7018 - ETA: 2s - loss: 1.2076 - acc: 0.7022 - ETA: 2s - loss: 1.2058 - acc: 0.7026 - ETA: 2s - loss: 1.2041 - acc: 0.7027 - ETA: 2s - loss: 1.2033 - acc: 0.7027 - ETA: 2s - loss: 1.2015 - acc: 0.7028 - ETA: 2s - loss: 1.1993 - acc: 0.7032 - ETA: 2s - loss: 1.1977 - acc: 0.7031 - ETA: 2s - loss: 1.1970 - acc: 0.7033 - ETA: 1s - loss: 1.1958 - acc: 0.7036 - ETA: 1s - loss: 1.1947 - acc: 0.7037 - ETA: 1s - loss: 1.1925 - acc: 0.7042 - ETA: 1s - loss: 1.1915 - acc: 0.7043 - ETA: 1s - loss: 1.1895 - acc: 0.7045 - ETA: 1s - loss: 1.1886 - acc: 0.7049 - ETA: 1s - loss: 1.1861 - acc: 0.7054 - ETA: 0s - loss: 1.1834 - acc: 0.7060 - ETA: 0s - loss: 1.1806 - acc: 0.7066 - ETA: 0s - loss: 1.1800 - acc: 0.7067 - ETA: 0s - loss: 1.1775 - acc: 0.7071 - ETA: 0s - loss: 1.1760 - acc: 0.7073 - ETA: 0s - loss: 1.1744 - acc: 0.7077 - ETA: 0s - loss: 1.1731 - acc: 0.7077Epoch 00000: val_loss improved from inf to 0.64072, saving model to weights.best.InceptionV3.hdf5 6680/6680 [==============================] - 45s - loss: 1.1717 - acc: 0.7075 - val_loss: 0.6407 - val_acc: 0.8096 Epoch 2/20 6620/6680 [============================>.] - ETA: 12s - loss: 0.5847 - acc: 0.9000 - ETA: 6s - loss: 0.4102 - acc: 0.8900 - ETA: 6s - loss: 0.4246 - acc: 0.8875 - ETA: 6s - loss: 0.4827 - acc: 0.8682 - ETA: 6s - loss: 0.4443 - acc: 0.8714 - ETA: 5s - loss: 0.4014 - acc: 0.8853 - ETA: 5s - loss: 0.3667 - acc: 0.8975 - ETA: 5s - loss: 0.3532 - acc: 0.9000 - ETA: 5s - loss: 0.3571 - acc: 0.9000 - ETA: 5s - loss: 0.3674 - acc: 0.8948 - ETA: 5s - loss: 0.3645 - acc: 0.8984 - ETA: 5s - loss: 0.3623 - acc: 0.8986 - ETA: 5s - loss: 0.3797 - acc: 0.8908 - ETA: 5s - loss: 0.3864 - acc: 0.8902 - ETA: 5s - loss: 0.3717 - acc: 0.8966 - ETA: 5s - loss: 0.3750 - acc: 0.8936 - ETA: 5s - loss: 0.3792 - acc: 0.8930 - ETA: 5s - loss: 0.3805 - acc: 0.8906 - ETA: 4s - loss: 0.3977 - acc: 0.8833 - ETA: 4s - loss: 0.4269 - acc: 0.8779 - ETA: 4s - loss: 0.4385 - acc: 0.8754 - ETA: 4s - loss: 0.4383 - acc: 0.8750 - ETA: 4s - loss: 0.4352 - acc: 0.8754 - ETA: 4s - loss: 0.4303 - acc: 0.8767 - ETA: 4s - loss: 0.4381 - acc: 0.8731 - ETA: 4s - loss: 0.4491 - acc: 0.8710 - ETA: 4s - loss: 0.4538 - acc: 0.8702 - ETA: 4s - loss: 0.4567 - acc: 0.8678 - ETA: 4s - loss: 0.4571 - acc: 0.8661 - ETA: 4s - loss: 0.4598 - acc: 0.8634 - ETA: 4s - loss: 0.4624 - acc: 0.8635 - ETA: 4s - loss: 0.4722 - acc: 0.8596 - ETA: 4s - loss: 0.4679 - acc: 0.8617 - ETA: 3s - loss: 0.4620 - acc: 0.8642 - ETA: 3s - loss: 0.4602 - acc: 0.8642 - ETA: 3s - loss: 0.4662 - acc: 0.8612 - ETA: 3s - loss: 0.4624 - acc: 0.8621 - ETA: 3s - loss: 0.4693 - acc: 0.8596 - ETA: 3s - loss: 0.4731 - acc: 0.8577 - ETA: 3s - loss: 0.4724 - acc: 0.8575 - ETA: 3s - loss: 0.4742 - acc: 0.8562 - ETA: 3s - loss: 0.4739 - acc: 0.8560 - ETA: 3s - loss: 0.4761 - acc: 0.8544 - ETA: 3s - loss: 0.4759 - acc: 0.8543 - ETA: 3s - loss: 0.4794 - acc: 0.8542 - ETA: 3s - loss: 0.4775 - acc: 0.8554 - ETA: 3s - loss: 0.4760 - acc: 0.8553 - ETA: 3s - loss: 0.4793 - acc: 0.8551 - ETA: 2s - loss: 0.4751 - acc: 0.8559 - ETA: 2s - loss: 0.4750 - acc: 0.8564 - ETA: 2s - loss: 0.4781 - acc: 0.8557 - ETA: 2s - loss: 0.4739 - acc: 0.8558 - ETA: 2s - loss: 0.4752 - acc: 0.8554 - ETA: 2s - loss: 0.4759 - acc: 0.8550 - ETA: 2s - loss: 0.4809 - acc: 0.8546 - ETA: 2s - loss: 0.4773 - acc: 0.8559 - ETA: 2s - loss: 0.4804 - acc: 0.8544 - ETA: 2s - loss: 0.4764 - acc: 0.8551 - ETA: 2s - loss: 0.4781 - acc: 0.8547 - ETA: 2s - loss: 0.4783 - acc: 0.8547 - ETA: 2s - loss: 0.4801 - acc: 0.8546 - ETA: 2s - loss: 0.4777 - acc: 0.8557 - ETA: 1s - loss: 0.4748 - acc: 0.8560 - ETA: 1s - loss: 0.4730 - acc: 0.8573 - ETA: 1s - loss: 0.4733 - acc: 0.8574 - ETA: 1s - loss: 0.4751 - acc: 0.8573 - ETA: 1s - loss: 0.4715 - acc: 0.8576 - ETA: 1s - loss: 0.4743 - acc: 0.8568 - ETA: 1s - loss: 0.4736 - acc: 0.8563 - ETA: 1s - loss: 0.4778 - acc: 0.8553 - ETA: 1s - loss: 0.4767 - acc: 0.8555 - ETA: 1s - loss: 0.4781 - acc: 0.8552 - ETA: 1s - loss: 0.4755 - acc: 0.8555 - ETA: 1s - loss: 0.4768 - acc: 0.8550 - ETA: 1s - loss: 0.4748 - acc: 0.8559 - ETA: 1s - loss: 0.4756 - acc: 0.8560 - ETA: 1s - loss: 0.4767 - acc: 0.8558 - ETA: 1s - loss: 0.4751 - acc: 0.8572 - ETA: 0s - loss: 0.4747 - acc: 0.8572 - ETA: 0s - loss: 0.4719 - acc: 0.8575 - ETA: 0s - loss: 0.4732 - acc: 0.8577 - ETA: 0s - loss: 0.4722 - acc: 0.8583 - ETA: 0s - loss: 0.4694 - acc: 0.8589 - ETA: 0s - loss: 0.4710 - acc: 0.8583 - ETA: 0s - loss: 0.4690 - acc: 0.8580 - ETA: 0s - loss: 0.4688 - acc: 0.8577 - ETA: 0s - loss: 0.4701 - acc: 0.8581 - ETA: 0s - loss: 0.4707 - acc: 0.8579 - ETA: 0s - loss: 0.4709 - acc: 0.8579 - ETA: 0s - loss: 0.4701 - acc: 0.8575 - ETA: 0s - loss: 0.4717 - acc: 0.8570 - ETA: 0s - loss: 0.4724 - acc: 0.8563 - ETA: 0s - loss: 0.4723 - acc: 0.8562Epoch 00001: val_loss did not improve 6680/6680 [==============================] - 5s - loss: 0.4719 - acc: 0.8561 - val_loss: 0.6837 - val_acc: 0.8108 Epoch 3/20 6600/6680 [============================>.] - ETA: 6s - loss: 0.4983 - acc: 0.9500 - ETA: 5s - loss: 0.2428 - acc: 0.9375 - ETA: 5s - loss: 0.2301 - acc: 0.9437 - ETA: 5s - loss: 0.2256 - acc: 0.9458 - ETA: 5s - loss: 0.2608 - acc: 0.9312 - ETA: 4s - loss: 0.2755 - acc: 0.9275 - ETA: 4s - loss: 0.2882 - acc: 0.9208 - ETA: 4s - loss: 0.2736 - acc: 0.9232 - ETA: 4s - loss: 0.2701 - acc: 0.9210 - ETA: 4s - loss: 0.2706 - acc: 0.9200 - ETA: 4s - loss: 0.2974 - acc: 0.9115 - ETA: 4s - loss: 0.2897 - acc: 0.9140 - ETA: 4s - loss: 0.2809 - acc: 0.9138 - ETA: 4s - loss: 0.2858 - acc: 0.9147 - ETA: 4s - loss: 0.2916 - acc: 0.9120 - ETA: 4s - loss: 0.3144 - acc: 0.9043 - ETA: 4s - loss: 0.3110 - acc: 0.9056 - ETA: 4s - loss: 0.3097 - acc: 0.9045 - ETA: 4s - loss: 0.3139 - acc: 0.9022 - ETA: 4s - loss: 0.3054 - acc: 0.9055 - ETA: 3s - loss: 0.3198 - acc: 0.9006 - ETA: 3s - loss: 0.3318 - acc: 0.8994 - ETA: 3s - loss: 0.3286 - acc: 0.9000 - ETA: 3s - loss: 0.3272 - acc: 0.9011 - ETA: 3s - loss: 0.3312 - acc: 0.8978 - ETA: 3s - loss: 0.3307 - acc: 0.8964 - ETA: 3s - loss: 0.3267 - acc: 0.8965 - ETA: 3s - loss: 0.3226 - acc: 0.8981 - ETA: 3s - loss: 0.3280 - acc: 0.8959 - ETA: 3s - loss: 0.3246 - acc: 0.8978 - ETA: 3s - loss: 0.3310 - acc: 0.8966 - ETA: 3s - loss: 0.3339 - acc: 0.8950 - ETA: 3s - loss: 0.3328 - acc: 0.8952 - ETA: 3s - loss: 0.3461 - acc: 0.8918 - ETA: 3s - loss: 0.3441 - acc: 0.8920 - ETA: 3s - loss: 0.3458 - acc: 0.8915 - ETA: 3s - loss: 0.3430 - acc: 0.8904 - ETA: 2s - loss: 0.3446 - acc: 0.8903 - ETA: 2s - loss: 0.3441 - acc: 0.8905 - ETA: 2s - loss: 0.3420 - acc: 0.8908 - ETA: 2s - loss: 0.3386 - acc: 0.8913 - ETA: 2s - loss: 0.3368 - acc: 0.8919 - ETA: 2s - loss: 0.3356 - acc: 0.8927 - ETA: 2s - loss: 0.3366 - acc: 0.8929 - ETA: 2s - loss: 0.3353 - acc: 0.8933 - ETA: 2s - loss: 0.3402 - acc: 0.8929 - ETA: 2s - loss: 0.3405 - acc: 0.8925 - ETA: 2s - loss: 0.3411 - acc: 0.8924 - ETA: 2s - loss: 0.3413 - acc: 0.8926 - ETA: 2s - loss: 0.3429 - acc: 0.8924 - ETA: 2s - loss: 0.3520 - acc: 0.8911 - ETA: 2s - loss: 0.3514 - acc: 0.8912 - ETA: 2s - loss: 0.3500 - acc: 0.8909 - ETA: 1s - loss: 0.3508 - acc: 0.8909 - ETA: 1s - loss: 0.3523 - acc: 0.8910 - ETA: 1s - loss: 0.3509 - acc: 0.8912 - ETA: 1s - loss: 0.3535 - acc: 0.8909 - ETA: 1s - loss: 0.3520 - acc: 0.8915 - ETA: 1s - loss: 0.3510 - acc: 0.8914 - ETA: 1s - loss: 0.3504 - acc: 0.8918 - ETA: 1s - loss: 0.3507 - acc: 0.8917 - ETA: 1s - loss: 0.3531 - acc: 0.8908 - ETA: 1s - loss: 0.3582 - acc: 0.8893 - ETA: 1s - loss: 0.3570 - acc: 0.8895 - ETA: 1s - loss: 0.3569 - acc: 0.8902 - ETA: 1s - loss: 0.3543 - acc: 0.8909 - ETA: 1s - loss: 0.3558 - acc: 0.8903 - ETA: 1s - loss: 0.3556 - acc: 0.8902 - ETA: 1s - loss: 0.3554 - acc: 0.8896 - ETA: 1s - loss: 0.3571 - acc: 0.8894 - ETA: 0s - loss: 0.3572 - acc: 0.8897 - ETA: 0s - loss: 0.3571 - acc: 0.8899 - ETA: 0s - loss: 0.3610 - acc: 0.8891 - ETA: 0s - loss: 0.3579 - acc: 0.8901 - ETA: 0s - loss: 0.3567 - acc: 0.8899 - ETA: 0s - loss: 0.3575 - acc: 0.8899 - ETA: 0s - loss: 0.3560 - acc: 0.8905 - ETA: 0s - loss: 0.3565 - acc: 0.8903 - ETA: 0s - loss: 0.3560 - acc: 0.8905 - ETA: 0s - loss: 0.3551 - acc: 0.8904 - ETA: 0s - loss: 0.3541 - acc: 0.8904 - ETA: 0s - loss: 0.3534 - acc: 0.8903 - ETA: 0s - loss: 0.3546 - acc: 0.8899 - ETA: 0s - loss: 0.3551 - acc: 0.8899 - ETA: 0s - loss: 0.3541 - acc: 0.8903 - ETA: 0s - loss: 0.3546 - acc: 0.8902Epoch 00002: val_loss did not improve 6680/6680 [==============================] - 5s - loss: 0.3582 - acc: 0.8897 - val_loss: 0.6450 - val_acc: 0.8383 Epoch 4/20 6660/6680 [============================>.] - ETA: 5s - loss: 0.2810 - acc: 0.9000 - ETA: 4s - loss: 0.1850 - acc: 0.9300 - ETA: 4s - loss: 0.1877 - acc: 0.9278 - ETA: 4s - loss: 0.2624 - acc: 0.9154 - ETA: 4s - loss: 0.3056 - acc: 0.9059 - ETA: 4s - loss: 0.2990 - acc: 0.9000 - ETA: 4s - loss: 0.2731 - acc: 0.9062 - ETA: 4s - loss: 0.2535 - acc: 0.9111 - ETA: 4s - loss: 0.2470 - acc: 0.9145 - ETA: 4s - loss: 0.2451 - acc: 0.9186 - ETA: 4s - loss: 0.2346 - acc: 0.9231 - ETA: 4s - loss: 0.2483 - acc: 0.9186 - ETA: 4s - loss: 0.2639 - acc: 0.9196 - ETA: 4s - loss: 0.2633 - acc: 0.9194 - ETA: 4s - loss: 0.2562 - acc: 0.9202 - ETA: 4s - loss: 0.2472 - acc: 0.9214 - ETA: 4s - loss: 0.2460 - acc: 0.9217 - ETA: 4s - loss: 0.2552 - acc: 0.9195 - ETA: 4s - loss: 0.2555 - acc: 0.9206 - ETA: 4s - loss: 0.2525 - acc: 0.9225 - ETA: 4s - loss: 0.2485 - acc: 0.9227 - ETA: 4s - loss: 0.2402 - acc: 0.9247 - ETA: 3s - loss: 0.2487 - acc: 0.9232 - ETA: 3s - loss: 0.2456 - acc: 0.9244 - ETA: 3s - loss: 0.2450 - acc: 0.9247 - ETA: 3s - loss: 0.2549 - acc: 0.9228 - ETA: 3s - loss: 0.2514 - acc: 0.9237 - ETA: 3s - loss: 0.2598 - acc: 0.9219 - ETA: 3s - loss: 0.2587 - acc: 0.9218 - ETA: 3s - loss: 0.2541 - acc: 0.9231 - ETA: 3s - loss: 0.2563 - acc: 0.9229 - ETA: 3s - loss: 0.2587 - acc: 0.9218 - ETA: 3s - loss: 0.2623 - acc: 0.9190 - ETA: 3s - loss: 0.2624 - acc: 0.9181 - ETA: 3s - loss: 0.2629 - acc: 0.9181 - ETA: 3s - loss: 0.2649 - acc: 0.9184 - ETA: 3s - loss: 0.2653 - acc: 0.9192 - ETA: 3s - loss: 0.2658 - acc: 0.9191 - ETA: 3s - loss: 0.2648 - acc: 0.9183 - ETA: 3s - loss: 0.2658 - acc: 0.9183 - ETA: 3s - loss: 0.2666 - acc: 0.9182 - ETA: 3s - loss: 0.2673 - acc: 0.9182 - ETA: 3s - loss: 0.2649 - acc: 0.9192 - ETA: 3s - loss: 0.2658 - acc: 0.9185 - ETA: 3s - loss: 0.2657 - acc: 0.9185 - ETA: 3s - loss: 0.2651 - acc: 0.9178 - ETA: 3s - loss: 0.2663 - acc: 0.9174 - ETA: 2s - loss: 0.2651 - acc: 0.9174 - ETA: 2s - loss: 0.2654 - acc: 0.9177 - ETA: 2s - loss: 0.2648 - acc: 0.9180 - ETA: 2s - loss: 0.2648 - acc: 0.9177 - ETA: 2s - loss: 0.2667 - acc: 0.9171 - ETA: 2s - loss: 0.2659 - acc: 0.9171 - ETA: 2s - loss: 0.2650 - acc: 0.9179 - ETA: 2s - loss: 0.2635 - acc: 0.9182 - ETA: 2s - loss: 0.2619 - acc: 0.9187 - ETA: 2s - loss: 0.2622 - acc: 0.9184 - ETA: 2s - loss: 0.2627 - acc: 0.9184 - ETA: 2s - loss: 0.2632 - acc: 0.9186 - ETA: 2s - loss: 0.2622 - acc: 0.9191 - ETA: 2s - loss: 0.2597 - acc: 0.9198 - ETA: 2s - loss: 0.2607 - acc: 0.9195 - ETA: 2s - loss: 0.2668 - acc: 0.9190 - ETA: 2s - loss: 0.2709 - acc: 0.9182 - ETA: 2s - loss: 0.2711 - acc: 0.9165 - ETA: 2s - loss: 0.2715 - acc: 0.9158 - ETA: 2s - loss: 0.2714 - acc: 0.9160 - ETA: 1s - loss: 0.2702 - acc: 0.9159 - ETA: 1s - loss: 0.2722 - acc: 0.9156 - ETA: 1s - loss: 0.2700 - acc: 0.9162 - ETA: 1s - loss: 0.2705 - acc: 0.9157 - ETA: 1s - loss: 0.2701 - acc: 0.9155 - ETA: 1s - loss: 0.2686 - acc: 0.9156 - ETA: 1s - loss: 0.2691 - acc: 0.9154 - ETA: 1s - loss: 0.2711 - acc: 0.9149 - ETA: 1s - loss: 0.2747 - acc: 0.9147 - ETA: 1s - loss: 0.2795 - acc: 0.9145 - ETA: 1s - loss: 0.2788 - acc: 0.9147 - ETA: 1s - loss: 0.2786 - acc: 0.9146 - ETA: 1s - loss: 0.2774 - acc: 0.9152 - ETA: 1s - loss: 0.2778 - acc: 0.9150 - ETA: 1s - loss: 0.2784 - acc: 0.9150 - ETA: 0s - loss: 0.2774 - acc: 0.9156 - ETA: 0s - loss: 0.2787 - acc: 0.9154 - ETA: 0s - loss: 0.2773 - acc: 0.9154 - ETA: 0s - loss: 0.2766 - acc: 0.9156 - ETA: 0s - loss: 0.2775 - acc: 0.9151 - ETA: 0s - loss: 0.2753 - acc: 0.9158 - ETA: 0s - loss: 0.2742 - acc: 0.9162 - ETA: 0s - loss: 0.2750 - acc: 0.9158 - ETA: 0s - loss: 0.2782 - acc: 0.9153 - ETA: 0s - loss: 0.2772 - acc: 0.9155 - ETA: 0s - loss: 0.2762 - acc: 0.9157 - ETA: 0s - loss: 0.2774 - acc: 0.9152 - ETA: 0s - loss: 0.2775 - acc: 0.9149 - ETA: 0s - loss: 0.2816 - acc: 0.9141 - ETA: 0s - loss: 0.2838 - acc: 0.9132 - ETA: 0s - loss: 0.2840 - acc: 0.9129 - ETA: 0s - loss: 0.2876 - acc: 0.9122 - ETA: 0s - loss: 0.2873 - acc: 0.9124 - ETA: 0s - loss: 0.2871 - acc: 0.9123 - ETA: 0s - loss: 0.2877 - acc: 0.9122Epoch 00003: val_loss did not improve 6680/6680 [==============================] - 6s - loss: 0.2870 - acc: 0.9124 - val_loss: 0.6777 - val_acc: 0.8491 Epoch 5/20 6660/6680 [============================>.] - ETA: 5s - loss: 0.1769 - acc: 0.9000 - ETA: 5s - loss: 0.2604 - acc: 0.9375 - ETA: 5s - loss: 0.2189 - acc: 0.9286 - ETA: 5s - loss: 0.2409 - acc: 0.9100 - ETA: 5s - loss: 0.2046 - acc: 0.9269 - ETA: 5s - loss: 0.2061 - acc: 0.9281 - ETA: 5s - loss: 0.2487 - acc: 0.9158 - ETA: 5s - loss: 0.2563 - acc: 0.9205 - ETA: 5s - loss: 0.2385 - acc: 0.9260 - ETA: 5s - loss: 0.2422 - acc: 0.9268 - ETA: 5s - loss: 0.2265 - acc: 0.9306 - ETA: 5s - loss: 0.2375 - acc: 0.9265 - ETA: 5s - loss: 0.2225 - acc: 0.9324 - ETA: 5s - loss: 0.2307 - acc: 0.9300 - ETA: 5s - loss: 0.2228 - acc: 0.9326 - ETA: 5s - loss: 0.2460 - acc: 0.9315 - ETA: 5s - loss: 0.2466 - acc: 0.9296 - ETA: 5s - loss: 0.2388 - acc: 0.9308 - ETA: 4s - loss: 0.2331 - acc: 0.9312 - ETA: 4s - loss: 0.2353 - acc: 0.9322 - ETA: 4s - loss: 0.2232 - acc: 0.9365 - ETA: 4s - loss: 0.2210 - acc: 0.9348 - ETA: 4s - loss: 0.2202 - acc: 0.9350 - ETA: 4s - loss: 0.2234 - acc: 0.9329 - ETA: 4s - loss: 0.2246 - acc: 0.9329 - ETA: 4s - loss: 0.2253 - acc: 0.9331 - ETA: 4s - loss: 0.2266 - acc: 0.9339 - ETA: 4s - loss: 0.2204 - acc: 0.9347 - ETA: 4s - loss: 0.2192 - acc: 0.9341 - ETA: 4s - loss: 0.2182 - acc: 0.9330 - ETA: 4s - loss: 0.2176 - acc: 0.9335 - ETA: 4s - loss: 0.2134 - acc: 0.9347 - ETA: 3s - loss: 0.2241 - acc: 0.9295 - ETA: 3s - loss: 0.2229 - acc: 0.9298 - ETA: 3s - loss: 0.2214 - acc: 0.9299 - ETA: 3s - loss: 0.2175 - acc: 0.9309 - ETA: 3s - loss: 0.2280 - acc: 0.9292 - ETA: 3s - loss: 0.2309 - acc: 0.9285 - ETA: 3s - loss: 0.2341 - acc: 0.9274 - ETA: 3s - loss: 0.2335 - acc: 0.9268 - ETA: 3s - loss: 0.2338 - acc: 0.9262 - ETA: 3s - loss: 0.2331 - acc: 0.9256 - ETA: 3s - loss: 0.2312 - acc: 0.9261 - ETA: 3s - loss: 0.2290 - acc: 0.9266 - ETA: 3s - loss: 0.2271 - acc: 0.9268 - ETA: 3s - loss: 0.2283 - acc: 0.9262 - ETA: 3s - loss: 0.2275 - acc: 0.9260 - ETA: 3s - loss: 0.2302 - acc: 0.9248 - ETA: 3s - loss: 0.2311 - acc: 0.9250 - ETA: 3s - loss: 0.2304 - acc: 0.9248 - ETA: 3s - loss: 0.2318 - acc: 0.9253 - ETA: 2s - loss: 0.2294 - acc: 0.9258 - ETA: 2s - loss: 0.2268 - acc: 0.9268 - ETA: 2s - loss: 0.2259 - acc: 0.9269 - ETA: 2s - loss: 0.2268 - acc: 0.9265 - ETA: 2s - loss: 0.2272 - acc: 0.9266 - ETA: 2s - loss: 0.2264 - acc: 0.9270 - ETA: 2s - loss: 0.2284 - acc: 0.9271 - ETA: 2s - loss: 0.2303 - acc: 0.9266 - ETA: 2s - loss: 0.2310 - acc: 0.9265 - ETA: 2s - loss: 0.2320 - acc: 0.9266 - ETA: 2s - loss: 0.2307 - acc: 0.9267 - ETA: 2s - loss: 0.2299 - acc: 0.9265 - ETA: 2s - loss: 0.2287 - acc: 0.9269 - ETA: 2s - loss: 0.2279 - acc: 0.9272 - ETA: 2s - loss: 0.2278 - acc: 0.9273 - ETA: 2s - loss: 0.2268 - acc: 0.9274 - ETA: 2s - loss: 0.2262 - acc: 0.9275 - ETA: 2s - loss: 0.2263 - acc: 0.9276 - ETA: 2s - loss: 0.2270 - acc: 0.9276 - ETA: 1s - loss: 0.2283 - acc: 0.9273 - ETA: 1s - loss: 0.2296 - acc: 0.9271 - ETA: 1s - loss: 0.2295 - acc: 0.9274 - ETA: 1s - loss: 0.2309 - acc: 0.9269 - ETA: 1s - loss: 0.2317 - acc: 0.9274 - ETA: 1s - loss: 0.2296 - acc: 0.9281 - ETA: 1s - loss: 0.2285 - acc: 0.9284 - ETA: 1s - loss: 0.2301 - acc: 0.9282 - ETA: 1s - loss: 0.2290 - acc: 0.9283 - ETA: 1s - loss: 0.2298 - acc: 0.9275 - ETA: 1s - loss: 0.2333 - acc: 0.9262 - ETA: 1s - loss: 0.2336 - acc: 0.9261 - ETA: 1s - loss: 0.2336 - acc: 0.9260 - ETA: 1s - loss: 0.2333 - acc: 0.9261 - ETA: 1s - loss: 0.2358 - acc: 0.9255 - ETA: 1s - loss: 0.2393 - acc: 0.9244 - ETA: 1s - loss: 0.2392 - acc: 0.9243 - ETA: 1s - loss: 0.2392 - acc: 0.9240 - ETA: 0s - loss: 0.2399 - acc: 0.9238 - ETA: 0s - loss: 0.2391 - acc: 0.9240 - ETA: 0s - loss: 0.2376 - acc: 0.9247 - ETA: 0s - loss: 0.2417 - acc: 0.9240 - ETA: 0s - loss: 0.2405 - acc: 0.9242 - ETA: 0s - loss: 0.2425 - acc: 0.9241 - ETA: 0s - loss: 0.2453 - acc: 0.9234 - ETA: 0s - loss: 0.2463 - acc: 0.9234 - ETA: 0s - loss: 0.2464 - acc: 0.9236 - ETA: 0s - loss: 0.2445 - acc: 0.9243 - ETA: 0s - loss: 0.2442 - acc: 0.9246 - ETA: 0s - loss: 0.2431 - acc: 0.9249 - ETA: 0s - loss: 0.2432 - acc: 0.9249 - ETA: 0s - loss: 0.2415 - acc: 0.9255 - ETA: 0s - loss: 0.2392 - acc: 0.9263Epoch 00004: val_loss did not improve 6680/6680 [==============================] - 6s - loss: 0.2398 - acc: 0.9263 - val_loss: 0.7026 - val_acc: 0.8467 Epoch 6/20 6640/6680 [============================>.] - ETA: 5s - loss: 0.0230 - acc: 1.0000 - ETA: 5s - loss: 0.0990 - acc: 0.9400 - ETA: 5s - loss: 0.1093 - acc: 0.9437 - ETA: 5s - loss: 0.1225 - acc: 0.9455 - ETA: 5s - loss: 0.1280 - acc: 0.9429 - ETA: 5s - loss: 0.1254 - acc: 0.9412 - ETA: 5s - loss: 0.1309 - acc: 0.9400 - ETA: 5s - loss: 0.1462 - acc: 0.9391 - ETA: 5s - loss: 0.1431 - acc: 0.9385 - ETA: 5s - loss: 0.1383 - acc: 0.9397 - ETA: 5s - loss: 0.1380 - acc: 0.9391 - ETA: 5s - loss: 0.1621 - acc: 0.9371 - ETA: 5s - loss: 0.1573 - acc: 0.9382 - ETA: 4s - loss: 0.1738 - acc: 0.9341 - ETA: 4s - loss: 0.1738 - acc: 0.9364 - ETA: 4s - loss: 0.1660 - acc: 0.9406 - ETA: 4s - loss: 0.1631 - acc: 0.9402 - ETA: 4s - loss: 0.1617 - acc: 0.9407 - ETA: 4s - loss: 0.1675 - acc: 0.9421 - ETA: 4s - loss: 0.1682 - acc: 0.9400 - ETA: 4s - loss: 0.1667 - acc: 0.9405 - ETA: 4s - loss: 0.1650 - acc: 0.9409 - ETA: 4s - loss: 0.1616 - acc: 0.9420 - ETA: 4s - loss: 0.1639 - acc: 0.9417 - ETA: 4s - loss: 0.1603 - acc: 0.9427 - ETA: 4s - loss: 0.1660 - acc: 0.9423 - ETA: 4s - loss: 0.1677 - acc: 0.9426 - ETA: 4s - loss: 0.1689 - acc: 0.9429 - ETA: 4s - loss: 0.1720 - acc: 0.9414 - ETA: 4s - loss: 0.1775 - acc: 0.9400 - ETA: 4s - loss: 0.1774 - acc: 0.9398 - ETA: 4s - loss: 0.1746 - acc: 0.9401 - ETA: 3s - loss: 0.1768 - acc: 0.9394 - ETA: 3s - loss: 0.1734 - acc: 0.9407 - ETA: 3s - loss: 0.1718 - acc: 0.9410 - ETA: 3s - loss: 0.1696 - acc: 0.9413 - ETA: 3s - loss: 0.1675 - acc: 0.9419 - ETA: 3s - loss: 0.1678 - acc: 0.9416 - ETA: 3s - loss: 0.1687 - acc: 0.9412 - ETA: 3s - loss: 0.1695 - acc: 0.9405 - ETA: 4s - loss: 0.1709 - acc: 0.9394 - ETA: 4s - loss: 0.1719 - acc: 0.9387 - ETA: 4s - loss: 0.1708 - acc: 0.9393 - ETA: 4s - loss: 0.1696 - acc: 0.9394 - ETA: 4s - loss: 0.1720 - acc: 0.9396 - ETA: 4s - loss: 0.1708 - acc: 0.9402 - ETA: 3s - loss: 0.1766 - acc: 0.9393 - ETA: 3s - loss: 0.1779 - acc: 0.9384 - ETA: 3s - loss: 0.1799 - acc: 0.9380 - ETA: 3s - loss: 0.1815 - acc: 0.9375 - ETA: 3s - loss: 0.1824 - acc: 0.9378 - ETA: 3s - loss: 0.1817 - acc: 0.9380 - ETA: 3s - loss: 0.1801 - acc: 0.9389 - ETA: 3s - loss: 0.1779 - acc: 0.9395 - ETA: 3s - loss: 0.1770 - acc: 0.9404 - ETA: 3s - loss: 0.1766 - acc: 0.9412 - ETA: 3s - loss: 0.1775 - acc: 0.9408 - ETA: 3s - loss: 0.1780 - acc: 0.9410 - ETA: 3s - loss: 0.1798 - acc: 0.9408 - ETA: 3s - loss: 0.1799 - acc: 0.9404 - ETA: 3s - loss: 0.1823 - acc: 0.9394 - ETA: 2s - loss: 0.1835 - acc: 0.9390 - ETA: 2s - loss: 0.1843 - acc: 0.9390 - ETA: 2s - loss: 0.1857 - acc: 0.9383 - ETA: 2s - loss: 0.1847 - acc: 0.9388 - ETA: 2s - loss: 0.1860 - acc: 0.9382 - ETA: 2s - loss: 0.1887 - acc: 0.9370 - ETA: 2s - loss: 0.1892 - acc: 0.9375 - ETA: 2s - loss: 0.1875 - acc: 0.9379 - ETA: 2s - loss: 0.1852 - acc: 0.9389 - ETA: 2s - loss: 0.1841 - acc: 0.9393 - ETA: 2s - loss: 0.1859 - acc: 0.9392 - ETA: 2s - loss: 0.1886 - acc: 0.9386 - ETA: 2s - loss: 0.1882 - acc: 0.9386 - ETA: 2s - loss: 0.1880 - acc: 0.9387 - ETA: 2s - loss: 0.1884 - acc: 0.9384 - ETA: 2s - loss: 0.1899 - acc: 0.9386 - ETA: 2s - loss: 0.1910 - acc: 0.9383 - ETA: 1s - loss: 0.1898 - acc: 0.9389 - ETA: 1s - loss: 0.1881 - acc: 0.9394 - ETA: 1s - loss: 0.1896 - acc: 0.9387 - ETA: 1s - loss: 0.1877 - acc: 0.9393 - ETA: 1s - loss: 0.1883 - acc: 0.9388 - ETA: 1s - loss: 0.1933 - acc: 0.9383 - ETA: 1s - loss: 0.1936 - acc: 0.9381 - ETA: 1s - loss: 0.1944 - acc: 0.9378 - ETA: 1s - loss: 0.1941 - acc: 0.9381 - ETA: 1s - loss: 0.1934 - acc: 0.9383 - ETA: 1s - loss: 0.1934 - acc: 0.9383 - ETA: 1s - loss: 0.1919 - acc: 0.9387 - ETA: 1s - loss: 0.1920 - acc: 0.9387 - ETA: 1s - loss: 0.1930 - acc: 0.9386 - ETA: 1s - loss: 0.1913 - acc: 0.9393 - ETA: 1s - loss: 0.1906 - acc: 0.9394 - ETA: 0s - loss: 0.1951 - acc: 0.9385 - ETA: 0s - loss: 0.1945 - acc: 0.9383 - ETA: 0s - loss: 0.1953 - acc: 0.9379 - ETA: 0s - loss: 0.1953 - acc: 0.9382 - ETA: 0s - loss: 0.1931 - acc: 0.9391 - ETA: 0s - loss: 0.1939 - acc: 0.9387 - ETA: 0s - loss: 0.1950 - acc: 0.9387 - ETA: 0s - loss: 0.1953 - acc: 0.9385 - ETA: 0s - loss: 0.1974 - acc: 0.9378 - ETA: 0s - loss: 0.1997 - acc: 0.9371 - ETA: 0s - loss: 0.1995 - acc: 0.9373 - ETA: 0s - loss: 0.2002 - acc: 0.9371 - ETA: 0s - loss: 0.1996 - acc: 0.9373 - ETA: 0s - loss: 0.1986 - acc: 0.9374 - ETA: 0s - loss: 0.1988 - acc: 0.9372Epoch 00005: val_loss did not improve 6680/6680 [==============================] - 6s - loss: 0.1991 - acc: 0.9370 - val_loss: 0.7065 - val_acc: 0.8479 Epoch 7/20 6660/6680 [============================>.] - ETA: 4s - loss: 0.2414 - acc: 0.8500 - ETA: 4s - loss: 0.2107 - acc: 0.9200 - ETA: 4s - loss: 0.1765 - acc: 0.9222 - ETA: 4s - loss: 0.1447 - acc: 0.9346 - ETA: 4s - loss: 0.1574 - acc: 0.9382 - ETA: 4s - loss: 0.1572 - acc: 0.9405 - ETA: 4s - loss: 0.1473 - acc: 0.9420 - ETA: 4s - loss: 0.1400 - acc: 0.9466 - ETA: 4s - loss: 0.1562 - acc: 0.9470 - ETA: 4s - loss: 0.1472 - acc: 0.9500 - ETA: 4s - loss: 0.1413 - acc: 0.9512 - ETA: 4s - loss: 0.1521 - acc: 0.9500 - ETA: 4s - loss: 0.1506 - acc: 0.9490 - ETA: 4s - loss: 0.1494 - acc: 0.9491 - ETA: 4s - loss: 0.1579 - acc: 0.9474 - ETA: 4s - loss: 0.1631 - acc: 0.9459 - ETA: 4s - loss: 0.1645 - acc: 0.9423 - ETA: 3s - loss: 0.1651 - acc: 0.9428 - ETA: 3s - loss: 0.1671 - acc: 0.9432 - ETA: 3s - loss: 0.1626 - acc: 0.9442 - ETA: 3s - loss: 0.1585 - acc: 0.9457 - ETA: 3s - loss: 0.1529 - acc: 0.9476 - ETA: 3s - loss: 0.1488 - acc: 0.9483 - ETA: 3s - loss: 0.1554 - acc: 0.9462 - ETA: 3s - loss: 0.1526 - acc: 0.9469 - ETA: 3s - loss: 0.1505 - acc: 0.9480 - ETA: 3s - loss: 0.1570 - acc: 0.9481 - ETA: 3s - loss: 0.1559 - acc: 0.9486 - ETA: 3s - loss: 0.1534 - acc: 0.9496 - ETA: 3s - loss: 0.1500 - acc: 0.9504 - ETA: 3s - loss: 0.1498 - acc: 0.9504 - ETA: 3s - loss: 0.1479 - acc: 0.9508 - ETA: 3s - loss: 0.1450 - acc: 0.9519 - ETA: 2s - loss: 0.1505 - acc: 0.9500 - ETA: 2s - loss: 0.1552 - acc: 0.9485 - ETA: 2s - loss: 0.1542 - acc: 0.9486 - ETA: 2s - loss: 0.1590 - acc: 0.9469 - ETA: 2s - loss: 0.1572 - acc: 0.9473 - ETA: 2s - loss: 0.1559 - acc: 0.9474 - ETA: 2s - loss: 0.1574 - acc: 0.9465 - ETA: 2s - loss: 0.1547 - acc: 0.9475 - ETA: 2s - loss: 0.1532 - acc: 0.9479 - ETA: 2s - loss: 0.1539 - acc: 0.9479 - ETA: 2s - loss: 0.1543 - acc: 0.9482 - ETA: 2s - loss: 0.1543 - acc: 0.9483 - ETA: 2s - loss: 0.1546 - acc: 0.9478 - ETA: 2s - loss: 0.1542 - acc: 0.9481 - ETA: 2s - loss: 0.1539 - acc: 0.9481 - ETA: 2s - loss: 0.1529 - acc: 0.9481 - ETA: 2s - loss: 0.1556 - acc: 0.9471 - ETA: 2s - loss: 0.1576 - acc: 0.9467 - ETA: 2s - loss: 0.1612 - acc: 0.9462 - ETA: 2s - loss: 0.1602 - acc: 0.9465 - ETA: 1s - loss: 0.1607 - acc: 0.9466 - ETA: 1s - loss: 0.1609 - acc: 0.9464 - ETA: 1s - loss: 0.1598 - acc: 0.9467 - ETA: 1s - loss: 0.1599 - acc: 0.9465 - ETA: 1s - loss: 0.1607 - acc: 0.9459 - ETA: 1s - loss: 0.1615 - acc: 0.9453 - ETA: 1s - loss: 0.1604 - acc: 0.9458 - ETA: 1s - loss: 0.1607 - acc: 0.9457 - ETA: 1s - loss: 0.1627 - acc: 0.9455 - ETA: 1s - loss: 0.1645 - acc: 0.9454 - ETA: 1s - loss: 0.1677 - acc: 0.9450 - ETA: 1s - loss: 0.1678 - acc: 0.9449 - ETA: 1s - loss: 0.1683 - acc: 0.9446 - ETA: 1s - loss: 0.1671 - acc: 0.9451 - ETA: 1s - loss: 0.1688 - acc: 0.9450 - ETA: 1s - loss: 0.1700 - acc: 0.9454 - ETA: 1s - loss: 0.1700 - acc: 0.9455 - ETA: 0s - loss: 0.1684 - acc: 0.9461 - ETA: 0s - loss: 0.1677 - acc: 0.9464 - ETA: 0s - loss: 0.1676 - acc: 0.9466 - ETA: 0s - loss: 0.1668 - acc: 0.9466 - ETA: 0s - loss: 0.1656 - acc: 0.9469 - ETA: 0s - loss: 0.1658 - acc: 0.9467 - ETA: 0s - loss: 0.1644 - acc: 0.9469 - ETA: 0s - loss: 0.1645 - acc: 0.9468 - ETA: 0s - loss: 0.1665 - acc: 0.9464 - ETA: 0s - loss: 0.1653 - acc: 0.9467 - ETA: 0s - loss: 0.1652 - acc: 0.9466 - ETA: 0s - loss: 0.1690 - acc: 0.9459 - ETA: 0s - loss: 0.1702 - acc: 0.9454 - ETA: 0s - loss: 0.1704 - acc: 0.9453 - ETA: 0s - loss: 0.1704 - acc: 0.9452 - ETA: 0s - loss: 0.1704 - acc: 0.9451 - ETA: 0s - loss: 0.1720 - acc: 0.9446Epoch 00006: val_loss did not improve 6680/6680 [==============================] - 5s - loss: 0.1724 - acc: 0.9445 - val_loss: 0.6832 - val_acc: 0.8515 Epoch 8/20 6640/6680 [============================>.] - ETA: 4s - loss: 0.2507 - acc: 0.9000 - ETA: 4s - loss: 0.1212 - acc: 0.9600 - ETA: 4s - loss: 0.0852 - acc: 0.9722 - ETA: 4s - loss: 0.1130 - acc: 0.9692 - ETA: 4s - loss: 0.1334 - acc: 0.9588 - ETA: 4s - loss: 0.1244 - acc: 0.9595 - ETA: 4s - loss: 0.1107 - acc: 0.9620 - ETA: 4s - loss: 0.1191 - acc: 0.9586 - ETA: 4s - loss: 0.1126 - acc: 0.9606 - ETA: 4s - loss: 0.1200 - acc: 0.9595 - ETA: 4s - loss: 0.1119 - acc: 0.9622 - ETA: 4s - loss: 0.1133 - acc: 0.9633 - ETA: 4s - loss: 0.1344 - acc: 0.9561 - ETA: 4s - loss: 0.1314 - acc: 0.9566 - ETA: 4s - loss: 0.1268 - acc: 0.9570 - ETA: 3s - loss: 0.1270 - acc: 0.9574 - ETA: 3s - loss: 0.1236 - acc: 0.9592 - ETA: 3s - loss: 0.1253 - acc: 0.9588 - ETA: 3s - loss: 0.1285 - acc: 0.9592 - ETA: 3s - loss: 0.1279 - acc: 0.9593 - ETA: 3s - loss: 0.1243 - acc: 0.9601 - ETA: 3s - loss: 0.1266 - acc: 0.9596 - ETA: 3s - loss: 0.1263 - acc: 0.9586 - ETA: 3s - loss: 0.1307 - acc: 0.9577 - ETA: 3s - loss: 0.1312 - acc: 0.9574 - ETA: 3s - loss: 0.1354 - acc: 0.9566 - ETA: 3s - loss: 0.1333 - acc: 0.9569 - ETA: 3s - loss: 0.1306 - acc: 0.9580 - ETA: 3s - loss: 0.1308 - acc: 0.9577 - ETA: 3s - loss: 0.1293 - acc: 0.9579 - ETA: 3s - loss: 0.1289 - acc: 0.9577 - ETA: 3s - loss: 0.1311 - acc: 0.9579 - ETA: 3s - loss: 0.1323 - acc: 0.9573 - ETA: 3s - loss: 0.1305 - acc: 0.9570 - ETA: 3s - loss: 0.1282 - acc: 0.9580 - ETA: 3s - loss: 0.1268 - acc: 0.9581 - ETA: 2s - loss: 0.1304 - acc: 0.9571 - ETA: 2s - loss: 0.1304 - acc: 0.9576 - ETA: 2s - loss: 0.1288 - acc: 0.9584 - ETA: 2s - loss: 0.1283 - acc: 0.9586 - ETA: 2s - loss: 0.1288 - acc: 0.9590 - ETA: 2s - loss: 0.1295 - acc: 0.9584 - ETA: 2s - loss: 0.1315 - acc: 0.9576 - ETA: 2s - loss: 0.1303 - acc: 0.9577 - ETA: 2s - loss: 0.1308 - acc: 0.9578 - ETA: 2s - loss: 0.1300 - acc: 0.9577 - ETA: 2s - loss: 0.1306 - acc: 0.9572 - ETA: 2s - loss: 0.1307 - acc: 0.9573 - ETA: 2s - loss: 0.1284 - acc: 0.9582 - ETA: 2s - loss: 0.1323 - acc: 0.9581 - ETA: 2s - loss: 0.1320 - acc: 0.9582 - ETA: 2s - loss: 0.1317 - acc: 0.9577 - ETA: 1s - loss: 0.1364 - acc: 0.9574 - ETA: 1s - loss: 0.1351 - acc: 0.9575 - ETA: 1s - loss: 0.1363 - acc: 0.9568 - ETA: 1s - loss: 0.1381 - acc: 0.9565 - ETA: 1s - loss: 0.1367 - acc: 0.9568 - ETA: 1s - loss: 0.1392 - acc: 0.9562 - ETA: 1s - loss: 0.1409 - acc: 0.9559 - ETA: 1s - loss: 0.1424 - acc: 0.9554 - ETA: 1s - loss: 0.1449 - acc: 0.9551 - ETA: 1s - loss: 0.1443 - acc: 0.9552 - ETA: 1s - loss: 0.1441 - acc: 0.9556 - ETA: 1s - loss: 0.1434 - acc: 0.9555 - ETA: 1s - loss: 0.1442 - acc: 0.9554 - ETA: 1s - loss: 0.1446 - acc: 0.9549 - ETA: 1s - loss: 0.1437 - acc: 0.9550 - ETA: 1s - loss: 0.1440 - acc: 0.9552 - ETA: 1s - loss: 0.1429 - acc: 0.9553 - ETA: 1s - loss: 0.1424 - acc: 0.9554 - ETA: 0s - loss: 0.1435 - acc: 0.9550 - ETA: 0s - loss: 0.1434 - acc: 0.9551 - ETA: 0s - loss: 0.1436 - acc: 0.9548 - ETA: 0s - loss: 0.1434 - acc: 0.9548 - ETA: 0s - loss: 0.1424 - acc: 0.9550 - ETA: 0s - loss: 0.1440 - acc: 0.9548 - ETA: 0s - loss: 0.1449 - acc: 0.9546 - ETA: 0s - loss: 0.1442 - acc: 0.9548 - ETA: 0s - loss: 0.1460 - acc: 0.9544 - ETA: 0s - loss: 0.1461 - acc: 0.9545 - ETA: 0s - loss: 0.1478 - acc: 0.9543 - ETA: 0s - loss: 0.1487 - acc: 0.9541 - ETA: 0s - loss: 0.1479 - acc: 0.9542 - ETA: 0s - loss: 0.1480 - acc: 0.9543 - ETA: 0s - loss: 0.1474 - acc: 0.9546 - ETA: 0s - loss: 0.1472 - acc: 0.9547Epoch 00007: val_loss did not improve 6680/6680 [==============================] - 5s - loss: 0.1470 - acc: 0.9546 - val_loss: 0.7338 - val_acc: 0.8587 Epoch 9/20 6620/6680 [============================>.] - ETA: 4s - loss: 0.0025 - acc: 1.0000 - ETA: 4s - loss: 0.0784 - acc: 0.9800 - ETA: 4s - loss: 0.0907 - acc: 0.9611 - ETA: 4s - loss: 0.1106 - acc: 0.9615 - ETA: 4s - loss: 0.1202 - acc: 0.9559 - ETA: 4s - loss: 0.1219 - acc: 0.9619 - ETA: 4s - loss: 0.1212 - acc: 0.9620 - ETA: 4s - loss: 0.1196 - acc: 0.9638 - ETA: 4s - loss: 0.1175 - acc: 0.9621 - ETA: 4s - loss: 0.1160 - acc: 0.9649 - ETA: 4s - loss: 0.1130 - acc: 0.9659 - ETA: 4s - loss: 0.1084 - acc: 0.9656 - ETA: 4s - loss: 0.1041 - acc: 0.9667 - ETA: 4s - loss: 0.1079 - acc: 0.9663 - ETA: 4s - loss: 0.1098 - acc: 0.9652 - ETA: 4s - loss: 0.1064 - acc: 0.9650 - ETA: 4s - loss: 0.1107 - acc: 0.9648 - ETA: 3s - loss: 0.1119 - acc: 0.9647 - ETA: 3s - loss: 0.1201 - acc: 0.9632 - ETA: 3s - loss: 0.1159 - acc: 0.9645 - ETA: 3s - loss: 0.1194 - acc: 0.9644 - ETA: 3s - loss: 0.1212 - acc: 0.9649 - ETA: 3s - loss: 0.1199 - acc: 0.9653 - ETA: 3s - loss: 0.1238 - acc: 0.9630 - ETA: 3s - loss: 0.1224 - acc: 0.9641 - ETA: 3s - loss: 0.1312 - acc: 0.9625 - ETA: 3s - loss: 0.1284 - acc: 0.9620 - ETA: 3s - loss: 0.1290 - acc: 0.9616 - ETA: 3s - loss: 0.1261 - acc: 0.9621 - ETA: 3s - loss: 0.1224 - acc: 0.9634 - ETA: 3s - loss: 0.1211 - acc: 0.9637 - ETA: 3s - loss: 0.1185 - acc: 0.9645 - ETA: 3s - loss: 0.1216 - acc: 0.9641 - ETA: 3s - loss: 0.1205 - acc: 0.9640 - ETA: 3s - loss: 0.1273 - acc: 0.9629 - ETA: 2s - loss: 0.1260 - acc: 0.9629 - ETA: 2s - loss: 0.1238 - acc: 0.9636 - ETA: 2s - loss: 0.1224 - acc: 0.9639 - ETA: 2s - loss: 0.1223 - acc: 0.9636 - ETA: 2s - loss: 0.1236 - acc: 0.9626 - ETA: 2s - loss: 0.1229 - acc: 0.9623 - ETA: 2s - loss: 0.1208 - acc: 0.9626 - ETA: 2s - loss: 0.1201 - acc: 0.9629 - ETA: 2s - loss: 0.1228 - acc: 0.9623 - ETA: 2s - loss: 0.1238 - acc: 0.9620 - ETA: 2s - loss: 0.1226 - acc: 0.9623 - ETA: 2s - loss: 0.1237 - acc: 0.9620 - ETA: 2s - loss: 0.1231 - acc: 0.9615 - ETA: 2s - loss: 0.1242 - acc: 0.9610 - ETA: 2s - loss: 0.1242 - acc: 0.9608 - ETA: 2s - loss: 0.1262 - acc: 0.9598 - ETA: 2s - loss: 0.1249 - acc: 0.9599 - ETA: 1s - loss: 0.1244 - acc: 0.9600 - ETA: 1s - loss: 0.1229 - acc: 0.9602 - ETA: 1s - loss: 0.1225 - acc: 0.9603 - ETA: 1s - loss: 0.1215 - acc: 0.9604 - ETA: 1s - loss: 0.1223 - acc: 0.9602 - ETA: 1s - loss: 0.1221 - acc: 0.9604 - ETA: 1s - loss: 0.1223 - acc: 0.9607 - ETA: 1s - loss: 0.1224 - acc: 0.9612 - ETA: 1s - loss: 0.1225 - acc: 0.9608 - ETA: 1s - loss: 0.1229 - acc: 0.9604 - ETA: 1s - loss: 0.1220 - acc: 0.9606 - ETA: 1s - loss: 0.1217 - acc: 0.9608 - ETA: 1s - loss: 0.1214 - acc: 0.9611 - ETA: 1s - loss: 0.1216 - acc: 0.9611 - ETA: 1s - loss: 0.1211 - acc: 0.9613 - ETA: 1s - loss: 0.1206 - acc: 0.9614 - ETA: 1s - loss: 0.1205 - acc: 0.9616 - ETA: 0s - loss: 0.1204 - acc: 0.9613 - ETA: 0s - loss: 0.1195 - acc: 0.9615 - ETA: 0s - loss: 0.1239 - acc: 0.9608 - ETA: 0s - loss: 0.1249 - acc: 0.9604 - ETA: 0s - loss: 0.1257 - acc: 0.9599 - ETA: 0s - loss: 0.1248 - acc: 0.9600 - ETA: 0s - loss: 0.1245 - acc: 0.9602 - ETA: 0s - loss: 0.1253 - acc: 0.9599 - ETA: 0s - loss: 0.1266 - acc: 0.9592 - ETA: 0s - loss: 0.1260 - acc: 0.9594 - ETA: 0s - loss: 0.1262 - acc: 0.9598 - ETA: 0s - loss: 0.1259 - acc: 0.9598 - ETA: 0s - loss: 0.1247 - acc: 0.9602 - ETA: 0s - loss: 0.1250 - acc: 0.9602 - ETA: 0s - loss: 0.1246 - acc: 0.9604 - ETA: 0s - loss: 0.1246 - acc: 0.9603Epoch 00008: val_loss did not improve 6680/6680 [==============================] - 5s - loss: 0.1247 - acc: 0.9605 - val_loss: 0.7781 - val_acc: 0.8383 Epoch 10/20 6640/6680 [============================>.] - ETA: 4s - loss: 0.1805 - acc: 0.9500 - ETA: 4s - loss: 0.0577 - acc: 0.9900 - ETA: 4s - loss: 0.0640 - acc: 0.9833 - ETA: 4s - loss: 0.0993 - acc: 0.9731 - ETA: 4s - loss: 0.0933 - acc: 0.9765 - ETA: 4s - loss: 0.1002 - acc: 0.9786 - ETA: 4s - loss: 0.0921 - acc: 0.9780 - ETA: 4s - loss: 0.0943 - acc: 0.9759 - ETA: 4s - loss: 0.0981 - acc: 0.9758 - ETA: 4s - loss: 0.1040 - acc: 0.9743 - ETA: 4s - loss: 0.0979 - acc: 0.9756 - ETA: 4s - loss: 0.0956 - acc: 0.9767 - ETA: 4s - loss: 0.0903 - acc: 0.9776 - ETA: 4s - loss: 0.0862 - acc: 0.9783 - ETA: 4s - loss: 0.0877 - acc: 0.9781 - ETA: 3s - loss: 0.0906 - acc: 0.9762 - ETA: 3s - loss: 0.0899 - acc: 0.9769 - ETA: 3s - loss: 0.0882 - acc: 0.9761 - ETA: 3s - loss: 0.0895 - acc: 0.9760 - ETA: 3s - loss: 0.0964 - acc: 0.9740 - ETA: 3s - loss: 0.0957 - acc: 0.9741 - ETA: 3s - loss: 0.0953 - acc: 0.9735 - ETA: 3s - loss: 0.0993 - acc: 0.9733 - ETA: 3s - loss: 0.0992 - acc: 0.9725 - ETA: 3s - loss: 0.0995 - acc: 0.9726 - ETA: 3s - loss: 0.1057 - acc: 0.9712 - ETA: 3s - loss: 0.1050 - acc: 0.9709 - ETA: 3s - loss: 0.1033 - acc: 0.9715 - ETA: 3s - loss: 0.1041 - acc: 0.9712 - ETA: 3s - loss: 0.1018 - acc: 0.9722 - ETA: 3s - loss: 0.1015 - acc: 0.9714 - ETA: 3s - loss: 0.1042 - acc: 0.9699 - ETA: 3s - loss: 0.1015 - acc: 0.9709 - ETA: 3s - loss: 0.1021 - acc: 0.9702 - ETA: 3s - loss: 0.1009 - acc: 0.9704 - ETA: 2s - loss: 0.1013 - acc: 0.9691 - ETA: 2s - loss: 0.0994 - acc: 0.9697 - ETA: 2s - loss: 0.0988 - acc: 0.9697 - ETA: 2s - loss: 0.1040 - acc: 0.9693 - ETA: 2s - loss: 0.1023 - acc: 0.9697 - ETA: 2s - loss: 0.1021 - acc: 0.9697 - ETA: 2s - loss: 0.1008 - acc: 0.9698 - ETA: 2s - loss: 0.0998 - acc: 0.9702 - ETA: 2s - loss: 0.0993 - acc: 0.9705 - ETA: 2s - loss: 0.1001 - acc: 0.9703 - ETA: 2s - loss: 0.0987 - acc: 0.9704 - ETA: 2s - loss: 0.0975 - acc: 0.9708 - ETA: 2s - loss: 0.0991 - acc: 0.9703 - ETA: 2s - loss: 0.0989 - acc: 0.9699 - ETA: 2s - loss: 0.0993 - acc: 0.9695 - ETA: 2s - loss: 0.0977 - acc: 0.9701 - ETA: 2s - loss: 0.0969 - acc: 0.9702 - ETA: 2s - loss: 0.0969 - acc: 0.9700 - ETA: 1s - loss: 0.0975 - acc: 0.9694 - ETA: 1s - loss: 0.0977 - acc: 0.9688 - ETA: 1s - loss: 0.0973 - acc: 0.9689 - ETA: 1s - loss: 0.0970 - acc: 0.9688 - ETA: 1s - loss: 0.0973 - acc: 0.9687 - ETA: 1s - loss: 0.0975 - acc: 0.9684 - ETA: 1s - loss: 0.0988 - acc: 0.9680 - ETA: 1s - loss: 0.0999 - acc: 0.9679 - ETA: 1s - loss: 0.1003 - acc: 0.9681 - ETA: 1s - loss: 0.1034 - acc: 0.9678 - ETA: 1s - loss: 0.1030 - acc: 0.9678 - ETA: 1s - loss: 0.1065 - acc: 0.9671 - ETA: 1s - loss: 0.1065 - acc: 0.9670 - ETA: 1s - loss: 0.1076 - acc: 0.9667 - ETA: 1s - loss: 0.1081 - acc: 0.9670 - ETA: 1s - loss: 0.1080 - acc: 0.9674 - ETA: 1s - loss: 0.1069 - acc: 0.9678 - ETA: 0s - loss: 0.1056 - acc: 0.9683 - ETA: 0s - loss: 0.1048 - acc: 0.9684 - ETA: 0s - loss: 0.1050 - acc: 0.9681 - ETA: 0s - loss: 0.1053 - acc: 0.9681 - ETA: 0s - loss: 0.1063 - acc: 0.9675 - ETA: 0s - loss: 0.1084 - acc: 0.9674 - ETA: 0s - loss: 0.1076 - acc: 0.9675 - ETA: 0s - loss: 0.1064 - acc: 0.9679 - ETA: 0s - loss: 0.1072 - acc: 0.9674 - ETA: 0s - loss: 0.1071 - acc: 0.9672 - ETA: 0s - loss: 0.1082 - acc: 0.9669 - ETA: 0s - loss: 0.1079 - acc: 0.9670 - ETA: 0s - loss: 0.1078 - acc: 0.9670 - ETA: 0s - loss: 0.1075 - acc: 0.9671 - ETA: 0s - loss: 0.1077 - acc: 0.9671 - ETA: 0s - loss: 0.1070 - acc: 0.9673Epoch 00009: val_loss did not improve 6680/6680 [==============================] - 5s - loss: 0.1073 - acc: 0.9671 - val_loss: 0.7530 - val_acc: 0.8611 Epoch 11/20 6600/6680 [============================>.] - ETA: 4s - loss: 0.0244 - acc: 1.0000 - ETA: 4s - loss: 0.0807 - acc: 0.9900 - ETA: 4s - loss: 0.0716 - acc: 0.9889 - ETA: 4s - loss: 0.0729 - acc: 0.9846 - ETA: 4s - loss: 0.0676 - acc: 0.9853 - ETA: 4s - loss: 0.0787 - acc: 0.9786 - ETA: 4s - loss: 0.0708 - acc: 0.9800 - ETA: 4s - loss: 0.0835 - acc: 0.9776 - ETA: 4s - loss: 0.0856 - acc: 0.9758 - ETA: 4s - loss: 0.0792 - acc: 0.9770 - ETA: 4s - loss: 0.0739 - acc: 0.9780 - ETA: 4s - loss: 0.0734 - acc: 0.9767 - ETA: 4s - loss: 0.0771 - acc: 0.9745 - ETA: 3s - loss: 0.0828 - acc: 0.9717 - ETA: 3s - loss: 0.0786 - acc: 0.9737 - ETA: 3s - loss: 0.0872 - acc: 0.9721 - ETA: 3s - loss: 0.0850 - acc: 0.9731 - ETA: 3s - loss: 0.0824 - acc: 0.9739 - ETA: 3s - loss: 0.0790 - acc: 0.9747 - ETA: 3s - loss: 0.0788 - acc: 0.9747 - ETA: 3s - loss: 0.0758 - acc: 0.9759 - ETA: 3s - loss: 0.0729 - acc: 0.9771 - ETA: 3s - loss: 0.0737 - acc: 0.9770 - ETA: 3s - loss: 0.0718 - acc: 0.9780 - ETA: 3s - loss: 0.0712 - acc: 0.9784 - ETA: 3s - loss: 0.0697 - acc: 0.9787 - ETA: 3s - loss: 0.0742 - acc: 0.9786 - ETA: 3s - loss: 0.0748 - acc: 0.9775 - ETA: 3s - loss: 0.0759 - acc: 0.9774 - ETA: 3s - loss: 0.0740 - acc: 0.9782 - ETA: 3s - loss: 0.0787 - acc: 0.9756 - ETA: 2s - loss: 0.0767 - acc: 0.9764 - ETA: 2s - loss: 0.0764 - acc: 0.9760 - ETA: 2s - loss: 0.0756 - acc: 0.9759 - ETA: 2s - loss: 0.0748 - acc: 0.9763 - ETA: 2s - loss: 0.0734 - acc: 0.9770 - ETA: 2s - loss: 0.0741 - acc: 0.9759 - ETA: 2s - loss: 0.0759 - acc: 0.9755 - ETA: 2s - loss: 0.0772 - acc: 0.9752 - ETA: 2s - loss: 0.0772 - acc: 0.9752 - ETA: 2s - loss: 0.0811 - acc: 0.9739 - ETA: 2s - loss: 0.0830 - acc: 0.9727 - ETA: 2s - loss: 0.0819 - acc: 0.9728 - ETA: 2s - loss: 0.0826 - acc: 0.9725 - ETA: 2s - loss: 0.0851 - acc: 0.9720 - ETA: 2s - loss: 0.0835 - acc: 0.9727 - ETA: 2s - loss: 0.0827 - acc: 0.9727 - ETA: 2s - loss: 0.0832 - acc: 0.9730 - ETA: 2s - loss: 0.0842 - acc: 0.9728 - ETA: 1s - loss: 0.0850 - acc: 0.9721 - ETA: 1s - loss: 0.0835 - acc: 0.9726 - ETA: 1s - loss: 0.0830 - acc: 0.9728 - ETA: 1s - loss: 0.0826 - acc: 0.9731 - ETA: 1s - loss: 0.0819 - acc: 0.9731 - ETA: 1s - loss: 0.0832 - acc: 0.9729 - ETA: 1s - loss: 0.0830 - acc: 0.9730 - ETA: 1s - loss: 0.0846 - acc: 0.9725 - ETA: 1s - loss: 0.0850 - acc: 0.9726 - ETA: 1s - loss: 0.0839 - acc: 0.9731 - ETA: 1s - loss: 0.0834 - acc: 0.9729 - ETA: 1s - loss: 0.0843 - acc: 0.9729 - ETA: 1s - loss: 0.0839 - acc: 0.9730 - ETA: 1s - loss: 0.0843 - acc: 0.9728 - ETA: 1s - loss: 0.0845 - acc: 0.9726 - ETA: 1s - loss: 0.0835 - acc: 0.9730 - ETA: 1s - loss: 0.0834 - acc: 0.9731 - ETA: 1s - loss: 0.0840 - acc: 0.9729 - ETA: 0s - loss: 0.0851 - acc: 0.9728 - ETA: 0s - loss: 0.0884 - acc: 0.9722 - ETA: 0s - loss: 0.0887 - acc: 0.9721 - ETA: 0s - loss: 0.0897 - acc: 0.9718 - ETA: 0s - loss: 0.0888 - acc: 0.9722 - ETA: 0s - loss: 0.0898 - acc: 0.9722 - ETA: 0s - loss: 0.0901 - acc: 0.9721 - ETA: 0s - loss: 0.0913 - acc: 0.9718 - ETA: 0s - loss: 0.0905 - acc: 0.9721 - ETA: 0s - loss: 0.0899 - acc: 0.9723 - ETA: 0s - loss: 0.0891 - acc: 0.9725 - ETA: 0s - loss: 0.0894 - acc: 0.9727 - ETA: 0s - loss: 0.0886 - acc: 0.9730 - ETA: 0s - loss: 0.0879 - acc: 0.9732 - ETA: 0s - loss: 0.0904 - acc: 0.9728 - ETA: 0s - loss: 0.0899 - acc: 0.9729 - ETA: 0s - loss: 0.0906 - acc: 0.9726Epoch 00010: val_loss did not improve 6680/6680 [==============================] - 5s - loss: 0.0922 - acc: 0.9720 - val_loss: 0.8565 - val_acc: 0.8503 Epoch 12/20 6640/6680 [============================>.] - ETA: 4s - loss: 0.0250 - acc: 1.0000 - ETA: 4s - loss: 0.0195 - acc: 0.9900 - ETA: 4s - loss: 0.0373 - acc: 0.9833 - ETA: 4s - loss: 0.0477 - acc: 0.9769 - ETA: 4s - loss: 0.0509 - acc: 0.9735 - ETA: 4s - loss: 0.0871 - acc: 0.9690 - ETA: 4s - loss: 0.0776 - acc: 0.9720 - ETA: 4s - loss: 0.0732 - acc: 0.9724 - ETA: 4s - loss: 0.0673 - acc: 0.9742 - ETA: 4s - loss: 0.0647 - acc: 0.9757 - ETA: 4s - loss: 0.0652 - acc: 0.9756 - ETA: 4s - loss: 0.0611 - acc: 0.9778 - ETA: 4s - loss: 0.0675 - acc: 0.9745 - ETA: 4s - loss: 0.0678 - acc: 0.9745 - ETA: 4s - loss: 0.0640 - acc: 0.9763 - ETA: 4s - loss: 0.0646 - acc: 0.9754 - ETA: 4s - loss: 0.0678 - acc: 0.9746 - ETA: 4s - loss: 0.0650 - acc: 0.9761 - ETA: 4s - loss: 0.0701 - acc: 0.9753 - ETA: 3s - loss: 0.0672 - acc: 0.9766 - ETA: 3s - loss: 0.0684 - acc: 0.9747 - ETA: 3s - loss: 0.0706 - acc: 0.9753 - ETA: 3s - loss: 0.0694 - acc: 0.9756 - ETA: 3s - loss: 0.0736 - acc: 0.9750 - ETA: 3s - loss: 0.0773 - acc: 0.9734 - ETA: 3s - loss: 0.0754 - acc: 0.9735 - ETA: 3s - loss: 0.0734 - acc: 0.9740 - ETA: 3s - loss: 0.0749 - acc: 0.9736 - ETA: 3s - loss: 0.0748 - acc: 0.9737 - ETA: 3s - loss: 0.0727 - acc: 0.9746 - ETA: 3s - loss: 0.0714 - acc: 0.9746 - ETA: 3s - loss: 0.0750 - acc: 0.9734 - ETA: 3s - loss: 0.0747 - acc: 0.9730 - ETA: 3s - loss: 0.0753 - acc: 0.9735 - ETA: 3s - loss: 0.0752 - acc: 0.9735 - ETA: 2s - loss: 0.0740 - acc: 0.9739 - ETA: 2s - loss: 0.0726 - acc: 0.9747 - ETA: 2s - loss: 0.0721 - acc: 0.9750 - ETA: 2s - loss: 0.0713 - acc: 0.9750 - ETA: 2s - loss: 0.0700 - acc: 0.9753 - ETA: 2s - loss: 0.0700 - acc: 0.9750 - ETA: 2s - loss: 0.0692 - acc: 0.9753 - ETA: 2s - loss: 0.0698 - acc: 0.9750 - ETA: 2s - loss: 0.0699 - acc: 0.9750 - ETA: 2s - loss: 0.0707 - acc: 0.9744 - ETA: 2s - loss: 0.0722 - acc: 0.9742 - ETA: 2s - loss: 0.0711 - acc: 0.9745 - ETA: 2s - loss: 0.0721 - acc: 0.9739 - ETA: 2s - loss: 0.0711 - acc: 0.9742 - ETA: 2s - loss: 0.0699 - acc: 0.9747 - ETA: 2s - loss: 0.0696 - acc: 0.9747 - ETA: 1s - loss: 0.0714 - acc: 0.9745 - ETA: 1s - loss: 0.0708 - acc: 0.9748 - ETA: 1s - loss: 0.0705 - acc: 0.9750 - ETA: 1s - loss: 0.0712 - acc: 0.9752 - ETA: 1s - loss: 0.0729 - acc: 0.9745 - ETA: 1s - loss: 0.0739 - acc: 0.9746 - ETA: 1s - loss: 0.0757 - acc: 0.9739 - ETA: 1s - loss: 0.0754 - acc: 0.9744 - ETA: 1s - loss: 0.0774 - acc: 0.9744 - ETA: 1s - loss: 0.0771 - acc: 0.9742 - ETA: 1s - loss: 0.0786 - acc: 0.9742 - ETA: 1s - loss: 0.0781 - acc: 0.9740 - ETA: 1s - loss: 0.0788 - acc: 0.9734 - ETA: 1s - loss: 0.0780 - acc: 0.9738 - ETA: 1s - loss: 0.0796 - acc: 0.9738 - ETA: 1s - loss: 0.0787 - acc: 0.9742 - ETA: 0s - loss: 0.0788 - acc: 0.9744 - ETA: 0s - loss: 0.0779 - acc: 0.9748 - ETA: 0s - loss: 0.0777 - acc: 0.9746 - ETA: 0s - loss: 0.0772 - acc: 0.9746 - ETA: 0s - loss: 0.0769 - acc: 0.9745 - ETA: 0s - loss: 0.0764 - acc: 0.9747 - ETA: 0s - loss: 0.0759 - acc: 0.9748 - ETA: 0s - loss: 0.0780 - acc: 0.9743 - ETA: 0s - loss: 0.0779 - acc: 0.9745 - ETA: 0s - loss: 0.0772 - acc: 0.9747 - ETA: 0s - loss: 0.0771 - acc: 0.9748 - ETA: 0s - loss: 0.0769 - acc: 0.9747 - ETA: 0s - loss: 0.0762 - acc: 0.9750 - ETA: 0s - loss: 0.0756 - acc: 0.9752 - ETA: 0s - loss: 0.0758 - acc: 0.9750 - ETA: 0s - loss: 0.0765 - acc: 0.9752 - ETA: 0s - loss: 0.0766 - acc: 0.9752Epoch 00011: val_loss did not improve 6680/6680 [==============================] - 5s - loss: 0.0768 - acc: 0.9749 - val_loss: 0.8031 - val_acc: 0.8575 Epoch 13/20 6600/6680 [============================>.] - ETA: 5s - loss: 0.0849 - acc: 0.9000 - ETA: 4s - loss: 0.0623 - acc: 0.9700 - ETA: 5s - loss: 0.0515 - acc: 0.9750 - ETA: 5s - loss: 0.0525 - acc: 0.9727 - ETA: 5s - loss: 0.0509 - acc: 0.9750 - ETA: 5s - loss: 0.0428 - acc: 0.9794 - ETA: 5s - loss: 0.0417 - acc: 0.9810 - ETA: 5s - loss: 0.0491 - acc: 0.9800 - ETA: 5s - loss: 0.0560 - acc: 0.9786 - ETA: 5s - loss: 0.0508 - acc: 0.9806 - ETA: 4s - loss: 0.0475 - acc: 0.9824 - ETA: 4s - loss: 0.0543 - acc: 0.9811 - ETA: 4s - loss: 0.0507 - acc: 0.9825 - ETA: 4s - loss: 0.0526 - acc: 0.9830 - ETA: 4s - loss: 0.0563 - acc: 0.9830 - ETA: 4s - loss: 0.0629 - acc: 0.9804 - ETA: 4s - loss: 0.0595 - acc: 0.9818 - ETA: 4s - loss: 0.0587 - acc: 0.9819 - ETA: 4s - loss: 0.0619 - acc: 0.9803 - ETA: 4s - loss: 0.0602 - acc: 0.9808 - ETA: 4s - loss: 0.0647 - acc: 0.9801 - ETA: 4s - loss: 0.0643 - acc: 0.9803 - ETA: 4s - loss: 0.0654 - acc: 0.9791 - ETA: 4s - loss: 0.0634 - acc: 0.9799 - ETA: 4s - loss: 0.0609 - acc: 0.9809 - ETA: 4s - loss: 0.0593 - acc: 0.9812 - ETA: 4s - loss: 0.0590 - acc: 0.9815 - ETA: 3s - loss: 0.0604 - acc: 0.9812 - ETA: 3s - loss: 0.0630 - acc: 0.9809 - ETA: 3s - loss: 0.0608 - acc: 0.9817 - ETA: 3s - loss: 0.0599 - acc: 0.9819 - ETA: 3s - loss: 0.0606 - acc: 0.9817 - ETA: 3s - loss: 0.0590 - acc: 0.9823 - ETA: 3s - loss: 0.0587 - acc: 0.9821 - ETA: 3s - loss: 0.0586 - acc: 0.9818 - ETA: 3s - loss: 0.0575 - acc: 0.9824 - ETA: 3s - loss: 0.0614 - acc: 0.9818 - ETA: 3s - loss: 0.0608 - acc: 0.9823 - ETA: 3s - loss: 0.0613 - acc: 0.9824 - ETA: 3s - loss: 0.0601 - acc: 0.9827 - ETA: 3s - loss: 0.0595 - acc: 0.9825 - ETA: 3s - loss: 0.0587 - acc: 0.9827 - ETA: 2s - loss: 0.0582 - acc: 0.9828 - ETA: 2s - loss: 0.0574 - acc: 0.9829 - ETA: 2s - loss: 0.0564 - acc: 0.9830 - ETA: 2s - loss: 0.0609 - acc: 0.9822 - ETA: 2s - loss: 0.0605 - acc: 0.9823 - ETA: 2s - loss: 0.0599 - acc: 0.9824 - ETA: 2s - loss: 0.0596 - acc: 0.9825 - ETA: 2s - loss: 0.0602 - acc: 0.9820 - ETA: 2s - loss: 0.0624 - acc: 0.9816 - ETA: 2s - loss: 0.0615 - acc: 0.9817 - ETA: 2s - loss: 0.0618 - acc: 0.9818 - ETA: 2s - loss: 0.0616 - acc: 0.9817 - ETA: 2s - loss: 0.0632 - acc: 0.9813 - ETA: 2s - loss: 0.0630 - acc: 0.9814 - ETA: 2s - loss: 0.0649 - acc: 0.9808 - ETA: 1s - loss: 0.0657 - acc: 0.9807 - ETA: 1s - loss: 0.0650 - acc: 0.9808 - ETA: 1s - loss: 0.0666 - acc: 0.9805 - ETA: 1s - loss: 0.0670 - acc: 0.9804 - ETA: 1s - loss: 0.0683 - acc: 0.9803 - ETA: 1s - loss: 0.0696 - acc: 0.9802 - ETA: 1s - loss: 0.0693 - acc: 0.9803 - ETA: 1s - loss: 0.0707 - acc: 0.9797 - ETA: 1s - loss: 0.0721 - acc: 0.9793 - ETA: 1s - loss: 0.0717 - acc: 0.9793 - ETA: 1s - loss: 0.0714 - acc: 0.9794 - ETA: 1s - loss: 0.0710 - acc: 0.9793 - ETA: 1s - loss: 0.0710 - acc: 0.9794 - ETA: 1s - loss: 0.0711 - acc: 0.9793 - ETA: 1s - loss: 0.0711 - acc: 0.9793 - ETA: 1s - loss: 0.0702 - acc: 0.9796 - ETA: 1s - loss: 0.0698 - acc: 0.9797 - ETA: 0s - loss: 0.0713 - acc: 0.9795 - ETA: 0s - loss: 0.0722 - acc: 0.9792 - ETA: 0s - loss: 0.0727 - acc: 0.9792 - ETA: 0s - loss: 0.0719 - acc: 0.9794 - ETA: 0s - loss: 0.0713 - acc: 0.9796 - ETA: 0s - loss: 0.0718 - acc: 0.9793 - ETA: 0s - loss: 0.0722 - acc: 0.9790 - ETA: 0s - loss: 0.0724 - acc: 0.9788 - ETA: 0s - loss: 0.0728 - acc: 0.9784 - ETA: 0s - loss: 0.0721 - acc: 0.9787 - ETA: 0s - loss: 0.0720 - acc: 0.9785 - ETA: 0s - loss: 0.0722 - acc: 0.9783 - ETA: 0s - loss: 0.0716 - acc: 0.9784 - ETA: 0s - loss: 0.0723 - acc: 0.9782 - ETA: 0s - loss: 0.0719 - acc: 0.9782Epoch 00012: val_loss did not improve 6680/6680 [==============================] - 5s - loss: 0.0731 - acc: 0.9781 - val_loss: 0.7755 - val_acc: 0.8647 Epoch 14/20 6660/6680 [============================>.] - ETA: 4s - loss: 0.1340 - acc: 0.9000 - ETA: 4s - loss: 0.0348 - acc: 0.9800 - ETA: 4s - loss: 0.0644 - acc: 0.9778 - ETA: 4s - loss: 0.0545 - acc: 0.9808 - ETA: 4s - loss: 0.0771 - acc: 0.9794 - ETA: 4s - loss: 0.0838 - acc: 0.9762 - ETA: 4s - loss: 0.0733 - acc: 0.9800 - ETA: 4s - loss: 0.0674 - acc: 0.9810 - ETA: 4s - loss: 0.0683 - acc: 0.9803 - ETA: 4s - loss: 0.0667 - acc: 0.9811 - ETA: 4s - loss: 0.0729 - acc: 0.9793 - ETA: 4s - loss: 0.0706 - acc: 0.9789 - ETA: 4s - loss: 0.0653 - acc: 0.9806 - ETA: 3s - loss: 0.0618 - acc: 0.9821 - ETA: 3s - loss: 0.0635 - acc: 0.9816 - ETA: 3s - loss: 0.0602 - acc: 0.9828 - ETA: 3s - loss: 0.0603 - acc: 0.9831 - ETA: 3s - loss: 0.0661 - acc: 0.9826 - ETA: 3s - loss: 0.0636 - acc: 0.9836 - ETA: 3s - loss: 0.0660 - acc: 0.9831 - ETA: 3s - loss: 0.0629 - acc: 0.9840 - ETA: 3s - loss: 0.0629 - acc: 0.9835 - ETA: 3s - loss: 0.0606 - acc: 0.9843 - ETA: 3s - loss: 0.0603 - acc: 0.9844 - ETA: 3s - loss: 0.0611 - acc: 0.9845 - ETA: 3s - loss: 0.0604 - acc: 0.9847 - ETA: 3s - loss: 0.0598 - acc: 0.9843 - ETA: 3s - loss: 0.0660 - acc: 0.9830 - ETA: 3s - loss: 0.0651 - acc: 0.9832 - ETA: 3s - loss: 0.0661 - acc: 0.9829 - ETA: 3s - loss: 0.0653 - acc: 0.9826 - ETA: 2s - loss: 0.0644 - acc: 0.9824 - ETA: 2s - loss: 0.0644 - acc: 0.9822 - ETA: 2s - loss: 0.0638 - acc: 0.9823 - ETA: 2s - loss: 0.0682 - acc: 0.9821 - ETA: 2s - loss: 0.0669 - acc: 0.9826 - ETA: 2s - loss: 0.0656 - acc: 0.9828 - ETA: 2s - loss: 0.0663 - acc: 0.9829 - ETA: 2s - loss: 0.0650 - acc: 0.9833 - ETA: 2s - loss: 0.0634 - acc: 0.9838 - ETA: 2s - loss: 0.0625 - acc: 0.9842 - ETA: 2s - loss: 0.0622 - acc: 0.9842 - ETA: 2s - loss: 0.0610 - acc: 0.9846 - ETA: 2s - loss: 0.0598 - acc: 0.9850 - ETA: 2s - loss: 0.0600 - acc: 0.9847 - ETA: 2s - loss: 0.0595 - acc: 0.9848 - ETA: 2s - loss: 0.0595 - acc: 0.9846 - ETA: 2s - loss: 0.0589 - acc: 0.9849 - ETA: 2s - loss: 0.0581 - acc: 0.9850 - ETA: 1s - loss: 0.0572 - acc: 0.9853 - ETA: 1s - loss: 0.0564 - acc: 0.9856 - ETA: 1s - loss: 0.0572 - acc: 0.9851 - ETA: 1s - loss: 0.0590 - acc: 0.9842 - ETA: 1s - loss: 0.0609 - acc: 0.9840 - ETA: 1s - loss: 0.0613 - acc: 0.9839 - ETA: 1s - loss: 0.0625 - acc: 0.9833 - ETA: 1s - loss: 0.0619 - acc: 0.9836 - ETA: 1s - loss: 0.0629 - acc: 0.9834 - ETA: 1s - loss: 0.0633 - acc: 0.9835 - ETA: 1s - loss: 0.0625 - acc: 0.9838 - ETA: 1s - loss: 0.0617 - acc: 0.9838 - ETA: 1s - loss: 0.0610 - acc: 0.9841 - ETA: 1s - loss: 0.0608 - acc: 0.9841 - ETA: 1s - loss: 0.0618 - acc: 0.9838 - ETA: 1s - loss: 0.0614 - acc: 0.9837 - ETA: 1s - loss: 0.0612 - acc: 0.9837 - ETA: 0s - loss: 0.0614 - acc: 0.9836 - ETA: 0s - loss: 0.0615 - acc: 0.9835 - ETA: 0s - loss: 0.0619 - acc: 0.9833 - ETA: 0s - loss: 0.0622 - acc: 0.9832 - ETA: 0s - loss: 0.0619 - acc: 0.9831 - ETA: 0s - loss: 0.0618 - acc: 0.9832 - ETA: 0s - loss: 0.0626 - acc: 0.9829 - ETA: 0s - loss: 0.0625 - acc: 0.9826 - ETA: 0s - loss: 0.0632 - acc: 0.9823 - ETA: 0s - loss: 0.0625 - acc: 0.9826 - ETA: 0s - loss: 0.0620 - acc: 0.9826 - ETA: 0s - loss: 0.0618 - acc: 0.9825 - ETA: 0s - loss: 0.0614 - acc: 0.9826 - ETA: 0s - loss: 0.0607 - acc: 0.9828 - ETA: 0s - loss: 0.0612 - acc: 0.9827 - ETA: 0s - loss: 0.0607 - acc: 0.9829 - ETA: 0s - loss: 0.0605 - acc: 0.9830 - ETA: 0s - loss: 0.0617 - acc: 0.9826Epoch 00013: val_loss did not improve 6680/6680 [==============================] - 5s - loss: 0.0615 - acc: 0.9826 - val_loss: 0.8708 - val_acc: 0.8479 Epoch 15/20 6640/6680 [============================>.] - ETA: 5s - loss: 0.0390 - acc: 1.0000 - ETA: 5s - loss: 0.0207 - acc: 1.0000 - ETA: 5s - loss: 0.0136 - acc: 1.0000 - ETA: 5s - loss: 0.0119 - acc: 1.0000 - ETA: 5s - loss: 0.0165 - acc: 0.9937 - ETA: 4s - loss: 0.0152 - acc: 0.9950 - ETA: 4s - loss: 0.0153 - acc: 0.9958 - ETA: 4s - loss: 0.0202 - acc: 0.9946 - ETA: 4s - loss: 0.0389 - acc: 0.9906 - ETA: 4s - loss: 0.0359 - acc: 0.9917 - ETA: 4s - loss: 0.0343 - acc: 0.9925 - ETA: 4s - loss: 0.0321 - acc: 0.9932 - ETA: 4s - loss: 0.0311 - acc: 0.9937 - ETA: 4s - loss: 0.0310 - acc: 0.9923 - ETA: 4s - loss: 0.0362 - acc: 0.9911 - ETA: 4s - loss: 0.0357 - acc: 0.9908 - ETA: 4s - loss: 0.0343 - acc: 0.9914 - ETA: 4s - loss: 0.0343 - acc: 0.9910 - ETA: 4s - loss: 0.0334 - acc: 0.9914 - ETA: 4s - loss: 0.0396 - acc: 0.9892 - ETA: 4s - loss: 0.0472 - acc: 0.9878 - ETA: 3s - loss: 0.0463 - acc: 0.9872 - ETA: 3s - loss: 0.0445 - acc: 0.9878 - ETA: 3s - loss: 0.0460 - acc: 0.9872 - ETA: 3s - loss: 0.0483 - acc: 0.9867 - ETA: 3s - loss: 0.0478 - acc: 0.9867 - ETA: 3s - loss: 0.0464 - acc: 0.9873 - ETA: 3s - loss: 0.0469 - acc: 0.9873 - ETA: 3s - loss: 0.0457 - acc: 0.9877 - ETA: 3s - loss: 0.0449 - acc: 0.9877 - ETA: 3s - loss: 0.0435 - acc: 0.9881 - ETA: 3s - loss: 0.0452 - acc: 0.9881 - ETA: 3s - loss: 0.0441 - acc: 0.9885 - ETA: 3s - loss: 0.0434 - acc: 0.9888 - ETA: 3s - loss: 0.0438 - acc: 0.9883 - ETA: 3s - loss: 0.0433 - acc: 0.9883 - ETA: 3s - loss: 0.0425 - acc: 0.9887 - ETA: 2s - loss: 0.0471 - acc: 0.9883 - ETA: 2s - loss: 0.0462 - acc: 0.9886 - ETA: 2s - loss: 0.0451 - acc: 0.9889 - ETA: 2s - loss: 0.0443 - acc: 0.9892 - ETA: 2s - loss: 0.0480 - acc: 0.9879 - ETA: 2s - loss: 0.0470 - acc: 0.9882 - ETA: 2s - loss: 0.0462 - acc: 0.9885 - ETA: 2s - loss: 0.0471 - acc: 0.9884 - ETA: 2s - loss: 0.0464 - acc: 0.9884 - ETA: 2s - loss: 0.0456 - acc: 0.9887 - ETA: 2s - loss: 0.0478 - acc: 0.9876 - ETA: 2s - loss: 0.0476 - acc: 0.9876 - ETA: 2s - loss: 0.0484 - acc: 0.9870 - ETA: 2s - loss: 0.0511 - acc: 0.9862 - ETA: 2s - loss: 0.0538 - acc: 0.9855 - ETA: 2s - loss: 0.0537 - acc: 0.9855 - ETA: 1s - loss: 0.0545 - acc: 0.9856 - ETA: 1s - loss: 0.0553 - acc: 0.9856 - ETA: 1s - loss: 0.0550 - acc: 0.9856 - ETA: 1s - loss: 0.0556 - acc: 0.9855 - ETA: 1s - loss: 0.0558 - acc: 0.9853 - ETA: 1s - loss: 0.0552 - acc: 0.9855 - ETA: 1s - loss: 0.0552 - acc: 0.9855 - ETA: 1s - loss: 0.0545 - acc: 0.9857 - ETA: 1s - loss: 0.0539 - acc: 0.9860 - ETA: 1s - loss: 0.0539 - acc: 0.9860 - ETA: 1s - loss: 0.0540 - acc: 0.9856 - ETA: 1s - loss: 0.0544 - acc: 0.9857 - ETA: 1s - loss: 0.0555 - acc: 0.9853 - ETA: 1s - loss: 0.0548 - acc: 0.9855 - ETA: 1s - loss: 0.0546 - acc: 0.9855 - ETA: 1s - loss: 0.0543 - acc: 0.9853 - ETA: 1s - loss: 0.0538 - acc: 0.9856 - ETA: 0s - loss: 0.0536 - acc: 0.9854 - ETA: 0s - loss: 0.0540 - acc: 0.9853 - ETA: 0s - loss: 0.0548 - acc: 0.9848 - ETA: 0s - loss: 0.0547 - acc: 0.9848 - ETA: 0s - loss: 0.0550 - acc: 0.9847 - ETA: 0s - loss: 0.0544 - acc: 0.9849 - ETA: 0s - loss: 0.0555 - acc: 0.9847 - ETA: 0s - loss: 0.0551 - acc: 0.9847 - ETA: 0s - loss: 0.0554 - acc: 0.9843 - ETA: 0s - loss: 0.0557 - acc: 0.9841 - ETA: 0s - loss: 0.0559 - acc: 0.9840 - ETA: 0s - loss: 0.0552 - acc: 0.9842 - ETA: 0s - loss: 0.0548 - acc: 0.9844 - ETA: 0s - loss: 0.0542 - acc: 0.9846 - ETA: 0s - loss: 0.0543 - acc: 0.9845 - ETA: 0s - loss: 0.0538 - acc: 0.9846Epoch 00014: val_loss did not improve 6680/6680 [==============================] - 5s - loss: 0.0537 - acc: 0.9846 - val_loss: 0.8716 - val_acc: 0.8515 Epoch 16/20 6660/6680 [============================>.] - ETA: 4s - loss: 0.0075 - acc: 1.0000 - ETA: 4s - loss: 0.0248 - acc: 0.9900 - ETA: 4s - loss: 0.0533 - acc: 0.9833 - ETA: 4s - loss: 0.0417 - acc: 0.9885 - ETA: 4s - loss: 0.0421 - acc: 0.9882 - ETA: 4s - loss: 0.0366 - acc: 0.9905 - ETA: 4s - loss: 0.0399 - acc: 0.9860 - ETA: 4s - loss: 0.0398 - acc: 0.9845 - ETA: 4s - loss: 0.0434 - acc: 0.9833 - ETA: 4s - loss: 0.0411 - acc: 0.9851 - ETA: 4s - loss: 0.0400 - acc: 0.9841 - ETA: 4s - loss: 0.0382 - acc: 0.9856 - ETA: 4s - loss: 0.0361 - acc: 0.9867 - ETA: 4s - loss: 0.0381 - acc: 0.9858 - ETA: 3s - loss: 0.0365 - acc: 0.9860 - ETA: 3s - loss: 0.0368 - acc: 0.9861 - ETA: 3s - loss: 0.0350 - acc: 0.9869 - ETA: 3s - loss: 0.0345 - acc: 0.9870 - ETA: 3s - loss: 0.0331 - acc: 0.9877 - ETA: 3s - loss: 0.0369 - acc: 0.9877 - ETA: 3s - loss: 0.0356 - acc: 0.9883 - ETA: 3s - loss: 0.0366 - acc: 0.9871 - ETA: 3s - loss: 0.0352 - acc: 0.9876 - ETA: 3s - loss: 0.0387 - acc: 0.9866 - ETA: 3s - loss: 0.0372 - acc: 0.9871 - ETA: 3s - loss: 0.0364 - acc: 0.9876 - ETA: 3s - loss: 0.0358 - acc: 0.9881 - ETA: 3s - loss: 0.0348 - acc: 0.9885 - ETA: 3s - loss: 0.0346 - acc: 0.9885 - ETA: 3s - loss: 0.0337 - acc: 0.9889 - ETA: 3s - loss: 0.0336 - acc: 0.9888 - ETA: 3s - loss: 0.0341 - acc: 0.9888 - ETA: 2s - loss: 0.0340 - acc: 0.9888 - ETA: 2s - loss: 0.0355 - acc: 0.9887 - ETA: 2s - loss: 0.0359 - acc: 0.9883 - ETA: 2s - loss: 0.0364 - acc: 0.9883 - ETA: 2s - loss: 0.0368 - acc: 0.9883 - ETA: 2s - loss: 0.0362 - acc: 0.9886 - ETA: 2s - loss: 0.0359 - acc: 0.9886 - ETA: 2s - loss: 0.0352 - acc: 0.9889 - ETA: 2s - loss: 0.0376 - acc: 0.9885 - ETA: 2s - loss: 0.0386 - acc: 0.9882 - ETA: 2s - loss: 0.0380 - acc: 0.9885 - ETA: 2s - loss: 0.0401 - acc: 0.9879 - ETA: 2s - loss: 0.0401 - acc: 0.9879 - ETA: 2s - loss: 0.0402 - acc: 0.9876 - ETA: 2s - loss: 0.0425 - acc: 0.9873 - ETA: 2s - loss: 0.0418 - acc: 0.9876 - ETA: 2s - loss: 0.0419 - acc: 0.9876 - ETA: 1s - loss: 0.0442 - acc: 0.9873 - ETA: 1s - loss: 0.0435 - acc: 0.9876 - ETA: 1s - loss: 0.0456 - acc: 0.9866 - ETA: 1s - loss: 0.0469 - acc: 0.9861 - ETA: 1s - loss: 0.0464 - acc: 0.9862 - ETA: 1s - loss: 0.0460 - acc: 0.9864 - ETA: 1s - loss: 0.0454 - acc: 0.9867 - ETA: 1s - loss: 0.0447 - acc: 0.9869 - ETA: 1s - loss: 0.0447 - acc: 0.9869 - ETA: 1s - loss: 0.0445 - acc: 0.9869 - ETA: 1s - loss: 0.0459 - acc: 0.9865 - ETA: 1s - loss: 0.0455 - acc: 0.9867 - ETA: 1s - loss: 0.0457 - acc: 0.9865 - ETA: 1s - loss: 0.0452 - acc: 0.9867 - ETA: 1s - loss: 0.0456 - acc: 0.9866 - ETA: 1s - loss: 0.0463 - acc: 0.9864 - ETA: 1s - loss: 0.0465 - acc: 0.9862 - ETA: 0s - loss: 0.0467 - acc: 0.9860 - ETA: 0s - loss: 0.0463 - acc: 0.9862 - ETA: 0s - loss: 0.0463 - acc: 0.9863 - ETA: 0s - loss: 0.0470 - acc: 0.9857 - ETA: 0s - loss: 0.0466 - acc: 0.9858 - ETA: 0s - loss: 0.0462 - acc: 0.9860 - ETA: 0s - loss: 0.0497 - acc: 0.9856 - ETA: 0s - loss: 0.0491 - acc: 0.9858 - ETA: 0s - loss: 0.0500 - acc: 0.9854 - ETA: 0s - loss: 0.0494 - acc: 0.9855 - ETA: 0s - loss: 0.0491 - acc: 0.9856 - ETA: 0s - loss: 0.0489 - acc: 0.9856 - ETA: 0s - loss: 0.0490 - acc: 0.9855 - ETA: 0s - loss: 0.0489 - acc: 0.9855 - ETA: 0s - loss: 0.0486 - acc: 0.9855 - ETA: 0s - loss: 0.0482 - acc: 0.9857 - ETA: 0s - loss: 0.0477 - acc: 0.9859 - ETA: 0s - loss: 0.0477 - acc: 0.9857Epoch 00015: val_loss did not improve 6680/6680 [==============================] - 5s - loss: 0.0476 - acc: 0.9858 - val_loss: 0.8739 - val_acc: 0.8551 Epoch 17/20 6620/6680 [============================>.] - ETA: 4s - loss: 0.0603 - acc: 0.9500 - ETA: 4s - loss: 0.0211 - acc: 0.9900 - ETA: 4s - loss: 0.0160 - acc: 0.9944 - ETA: 4s - loss: 0.0123 - acc: 0.9962 - ETA: 4s - loss: 0.0138 - acc: 0.9941 - ETA: 4s - loss: 0.0132 - acc: 0.9952 - ETA: 4s - loss: 0.0142 - acc: 0.9940 - ETA: 4s - loss: 0.0135 - acc: 0.9948 - ETA: 4s - loss: 0.0156 - acc: 0.9939 - ETA: 4s - loss: 0.0213 - acc: 0.9919 - ETA: 4s - loss: 0.0239 - acc: 0.9902 - ETA: 4s - loss: 0.0229 - acc: 0.9911 - ETA: 4s - loss: 0.0250 - acc: 0.9908 - ETA: 4s - loss: 0.0278 - acc: 0.9896 - ETA: 4s - loss: 0.0264 - acc: 0.9904 - ETA: 3s - loss: 0.0284 - acc: 0.9893 - ETA: 3s - loss: 0.0337 - acc: 0.9869 - ETA: 3s - loss: 0.0342 - acc: 0.9870 - ETA: 3s - loss: 0.0335 - acc: 0.9877 - ETA: 3s - loss: 0.0325 - acc: 0.9883 - ETA: 3s - loss: 0.0330 - acc: 0.9877 - ETA: 3s - loss: 0.0339 - acc: 0.9876 - ETA: 3s - loss: 0.0345 - acc: 0.9876 - ETA: 3s - loss: 0.0356 - acc: 0.9876 - ETA: 3s - loss: 0.0349 - acc: 0.9875 - ETA: 3s - loss: 0.0358 - acc: 0.9874 - ETA: 3s - loss: 0.0375 - acc: 0.9869 - ETA: 3s - loss: 0.0386 - acc: 0.9860 - ETA: 3s - loss: 0.0383 - acc: 0.9856 - ETA: 3s - loss: 0.0386 - acc: 0.9857 - ETA: 3s - loss: 0.0393 - acc: 0.9853 - ETA: 3s - loss: 0.0384 - acc: 0.9858 - ETA: 3s - loss: 0.0375 - acc: 0.9862 - ETA: 3s - loss: 0.0365 - acc: 0.9866 - ETA: 3s - loss: 0.0364 - acc: 0.9867 - ETA: 2s - loss: 0.0358 - acc: 0.9867 - ETA: 2s - loss: 0.0350 - acc: 0.9871 - ETA: 2s - loss: 0.0353 - acc: 0.9867 - ETA: 2s - loss: 0.0345 - acc: 0.9871 - ETA: 2s - loss: 0.0347 - acc: 0.9868 - ETA: 2s - loss: 0.0374 - acc: 0.9855 - ETA: 2s - loss: 0.0371 - acc: 0.9855 - ETA: 2s - loss: 0.0370 - acc: 0.9855 - ETA: 2s - loss: 0.0362 - acc: 0.9858 - ETA: 2s - loss: 0.0356 - acc: 0.9861 - ETA: 2s - loss: 0.0351 - acc: 0.9864 - ETA: 2s - loss: 0.0345 - acc: 0.9867 - ETA: 2s - loss: 0.0342 - acc: 0.9868 - ETA: 2s - loss: 0.0350 - acc: 0.9868 - ETA: 2s - loss: 0.0352 - acc: 0.9868 - ETA: 2s - loss: 0.0346 - acc: 0.9871 - ETA: 2s - loss: 0.0343 - acc: 0.9871 - ETA: 1s - loss: 0.0337 - acc: 0.9873 - ETA: 1s - loss: 0.0344 - acc: 0.9873 - ETA: 1s - loss: 0.0342 - acc: 0.9872 - ETA: 1s - loss: 0.0351 - acc: 0.9870 - ETA: 1s - loss: 0.0361 - acc: 0.9870 - ETA: 1s - loss: 0.0357 - acc: 0.9870 - ETA: 1s - loss: 0.0355 - acc: 0.9872 - ETA: 1s - loss: 0.0353 - acc: 0.9873 - ETA: 1s - loss: 0.0355 - acc: 0.9873 - ETA: 1s - loss: 0.0351 - acc: 0.9874 - ETA: 1s - loss: 0.0347 - acc: 0.9877 - ETA: 1s - loss: 0.0344 - acc: 0.9877 - ETA: 1s - loss: 0.0341 - acc: 0.9878 - ETA: 1s - loss: 0.0342 - acc: 0.9878 - ETA: 1s - loss: 0.0349 - acc: 0.9876 - ETA: 1s - loss: 0.0346 - acc: 0.9877 - ETA: 1s - loss: 0.0354 - acc: 0.9875 - ETA: 1s - loss: 0.0361 - acc: 0.9871 - ETA: 1s - loss: 0.0415 - acc: 0.9861 - ETA: 0s - loss: 0.0418 - acc: 0.9859 - ETA: 0s - loss: 0.0419 - acc: 0.9858 - ETA: 0s - loss: 0.0416 - acc: 0.9860 - ETA: 0s - loss: 0.0411 - acc: 0.9862 - ETA: 0s - loss: 0.0407 - acc: 0.9863 - ETA: 0s - loss: 0.0404 - acc: 0.9865 - ETA: 0s - loss: 0.0406 - acc: 0.9863 - ETA: 0s - loss: 0.0405 - acc: 0.9861 - ETA: 0s - loss: 0.0401 - acc: 0.9863 - ETA: 0s - loss: 0.0399 - acc: 0.9863 - ETA: 0s - loss: 0.0398 - acc: 0.9864 - ETA: 0s - loss: 0.0394 - acc: 0.9865 - ETA: 0s - loss: 0.0411 - acc: 0.9862 - ETA: 0s - loss: 0.0408 - acc: 0.9864 - ETA: 0s - loss: 0.0413 - acc: 0.9859 - ETA: 0s - loss: 0.0412 - acc: 0.9858 - ETA: 0s - loss: 0.0415 - acc: 0.9853Epoch 00016: val_loss did not improve 6680/6680 [==============================] - 5s - loss: 0.0412 - acc: 0.9855 - val_loss: 0.8735 - val_acc: 0.8551 Epoch 18/20 6640/6680 [============================>.] - ETA: 4s - loss: 0.0030 - acc: 1.0000 - ETA: 4s - loss: 0.0190 - acc: 0.9900 - ETA: 4s - loss: 0.0296 - acc: 0.9889 - ETA: 4s - loss: 0.0228 - acc: 0.9923 - ETA: 4s - loss: 0.0273 - acc: 0.9912 - ETA: 4s - loss: 0.0359 - acc: 0.9905 - ETA: 4s - loss: 0.0307 - acc: 0.9920 - ETA: 4s - loss: 0.0284 - acc: 0.9914 - ETA: 4s - loss: 0.0265 - acc: 0.9909 - ETA: 4s - loss: 0.0241 - acc: 0.9919 - ETA: 4s - loss: 0.0228 - acc: 0.9927 - ETA: 4s - loss: 0.0224 - acc: 0.9932 - ETA: 4s - loss: 0.0233 - acc: 0.9927 - ETA: 4s - loss: 0.0260 - acc: 0.9913 - ETA: 4s - loss: 0.0250 - acc: 0.9920 - ETA: 4s - loss: 0.0248 - acc: 0.9917 - ETA: 4s - loss: 0.0248 - acc: 0.9922 - ETA: 4s - loss: 0.0242 - acc: 0.9926 - ETA: 4s - loss: 0.0246 - acc: 0.9917 - ETA: 4s - loss: 0.0244 - acc: 0.9921 - ETA: 3s - loss: 0.0233 - acc: 0.9925 - ETA: 3s - loss: 0.0229 - acc: 0.9923 - ETA: 3s - loss: 0.0224 - acc: 0.9926 - ETA: 3s - loss: 0.0224 - acc: 0.9929 - ETA: 3s - loss: 0.0226 - acc: 0.9927 - ETA: 3s - loss: 0.0229 - acc: 0.9925 - ETA: 3s - loss: 0.0242 - acc: 0.9918 - ETA: 3s - loss: 0.0254 - acc: 0.9917 - ETA: 3s - loss: 0.0251 - acc: 0.9920 - ETA: 3s - loss: 0.0257 - acc: 0.9914 - ETA: 3s - loss: 0.0264 - acc: 0.9912 - ETA: 3s - loss: 0.0265 - acc: 0.9911 - ETA: 3s - loss: 0.0258 - acc: 0.9914 - ETA: 3s - loss: 0.0252 - acc: 0.9917 - ETA: 3s - loss: 0.0271 - acc: 0.9915 - ETA: 2s - loss: 0.0279 - acc: 0.9911 - ETA: 2s - loss: 0.0302 - acc: 0.9906 - ETA: 2s - loss: 0.0315 - acc: 0.9902 - ETA: 2s - loss: 0.0315 - acc: 0.9898 - ETA: 2s - loss: 0.0330 - acc: 0.9894 - ETA: 2s - loss: 0.0324 - acc: 0.9897 - ETA: 2s - loss: 0.0317 - acc: 0.9899 - ETA: 2s - loss: 0.0320 - acc: 0.9899 - ETA: 2s - loss: 0.0321 - acc: 0.9895 - ETA: 2s - loss: 0.0328 - acc: 0.9889 - ETA: 2s - loss: 0.0334 - acc: 0.9889 - ETA: 2s - loss: 0.0337 - acc: 0.9889 - ETA: 2s - loss: 0.0351 - acc: 0.9886 - ETA: 2s - loss: 0.0346 - acc: 0.9888 - ETA: 2s - loss: 0.0342 - acc: 0.9888 - ETA: 2s - loss: 0.0339 - acc: 0.9887 - ETA: 1s - loss: 0.0333 - acc: 0.9890 - ETA: 1s - loss: 0.0348 - acc: 0.9887 - ETA: 1s - loss: 0.0352 - acc: 0.9884 - ETA: 1s - loss: 0.0350 - acc: 0.9887 - ETA: 1s - loss: 0.0349 - acc: 0.9889 - ETA: 1s - loss: 0.0346 - acc: 0.9888 - ETA: 1s - loss: 0.0344 - acc: 0.9888 - ETA: 1s - loss: 0.0338 - acc: 0.9890 - ETA: 1s - loss: 0.0334 - acc: 0.9892 - ETA: 1s - loss: 0.0336 - acc: 0.9890 - ETA: 1s - loss: 0.0337 - acc: 0.9889 - ETA: 1s - loss: 0.0338 - acc: 0.9889 - ETA: 1s - loss: 0.0335 - acc: 0.9889 - ETA: 1s - loss: 0.0331 - acc: 0.9891 - ETA: 1s - loss: 0.0348 - acc: 0.9885 - ETA: 1s - loss: 0.0345 - acc: 0.9886 - ETA: 0s - loss: 0.0351 - acc: 0.9884 - ETA: 0s - loss: 0.0348 - acc: 0.9886 - ETA: 0s - loss: 0.0346 - acc: 0.9886 - ETA: 0s - loss: 0.0345 - acc: 0.9886 - ETA: 0s - loss: 0.0351 - acc: 0.9884 - ETA: 0s - loss: 0.0349 - acc: 0.9884 - ETA: 0s - loss: 0.0345 - acc: 0.9885 - ETA: 0s - loss: 0.0348 - acc: 0.9885 - ETA: 0s - loss: 0.0346 - acc: 0.9887 - ETA: 0s - loss: 0.0346 - acc: 0.9887 - ETA: 0s - loss: 0.0344 - acc: 0.9888 - ETA: 0s - loss: 0.0341 - acc: 0.9889 - ETA: 0s - loss: 0.0339 - acc: 0.9891 - ETA: 0s - loss: 0.0341 - acc: 0.9889 - ETA: 0s - loss: 0.0344 - acc: 0.9889 - ETA: 0s - loss: 0.0348 - acc: 0.9889 - ETA: 0s - loss: 0.0347 - acc: 0.9890Epoch 00017: val_loss did not improve 6680/6680 [==============================] - 5s - loss: 0.0346 - acc: 0.9891 - val_loss: 0.9533 - val_acc: 0.8515 Epoch 19/20 6620/6680 [============================>.] - ETA: 4s - loss: 8.9530e-04 - acc: 1.0000 - ETA: 4s - loss: 0.0325 - acc: 0.9900 - ETA: 4s - loss: 0.0197 - acc: 0.9944 - ETA: 4s - loss: 0.0172 - acc: 0.9923 - ETA: 4s - loss: 0.0168 - acc: 0.9941 - ETA: 4s - loss: 0.0140 - acc: 0.9952 - ETA: 4s - loss: 0.0120 - acc: 0.9960 - ETA: 4s - loss: 0.0164 - acc: 0.9948 - ETA: 4s - loss: 0.0177 - acc: 0.9939 - ETA: 4s - loss: 0.0162 - acc: 0.9946 - ETA: 4s - loss: 0.0200 - acc: 0.9927 - ETA: 4s - loss: 0.0186 - acc: 0.9933 - ETA: 4s - loss: 0.0179 - acc: 0.9939 - ETA: 4s - loss: 0.0176 - acc: 0.9934 - ETA: 3s - loss: 0.0165 - acc: 0.9939 - ETA: 3s - loss: 0.0184 - acc: 0.9926 - ETA: 3s - loss: 0.0177 - acc: 0.9931 - ETA: 3s - loss: 0.0179 - acc: 0.9928 - ETA: 3s - loss: 0.0172 - acc: 0.9932 - ETA: 3s - loss: 0.0191 - acc: 0.9922 - ETA: 3s - loss: 0.0188 - acc: 0.9926 - ETA: 3s - loss: 0.0192 - acc: 0.9924 - ETA: 3s - loss: 0.0186 - acc: 0.9927 - ETA: 3s - loss: 0.0202 - acc: 0.9919 - ETA: 3s - loss: 0.0195 - acc: 0.9923 - ETA: 3s - loss: 0.0198 - acc: 0.9921 - ETA: 3s - loss: 0.0202 - acc: 0.9919 - ETA: 3s - loss: 0.0218 - acc: 0.9917 - ETA: 3s - loss: 0.0222 - acc: 0.9916 - ETA: 3s - loss: 0.0224 - acc: 0.9915 - ETA: 3s - loss: 0.0222 - acc: 0.9913 - ETA: 3s - loss: 0.0217 - acc: 0.9916 - ETA: 2s - loss: 0.0225 - acc: 0.9915 - ETA: 2s - loss: 0.0224 - acc: 0.9914 - ETA: 2s - loss: 0.0250 - acc: 0.9909 - ETA: 2s - loss: 0.0257 - acc: 0.9908 - ETA: 2s - loss: 0.0258 - acc: 0.9907 - ETA: 2s - loss: 0.0254 - acc: 0.9909 - ETA: 2s - loss: 0.0251 - acc: 0.9908 - ETA: 2s - loss: 0.0254 - acc: 0.9908 - ETA: 2s - loss: 0.0252 - acc: 0.9910 - ETA: 2s - loss: 0.0246 - acc: 0.9912 - ETA: 2s - loss: 0.0246 - acc: 0.9911 - ETA: 2s - loss: 0.0243 - acc: 0.9913 - ETA: 2s - loss: 0.0238 - acc: 0.9915 - ETA: 2s - loss: 0.0266 - acc: 0.9912 - ETA: 2s - loss: 0.0280 - acc: 0.9905 - ETA: 2s - loss: 0.0276 - acc: 0.9907 - ETA: 2s - loss: 0.0271 - acc: 0.9909 - ETA: 1s - loss: 0.0272 - acc: 0.9906 - ETA: 1s - loss: 0.0290 - acc: 0.9903 - ETA: 1s - loss: 0.0287 - acc: 0.9902 - ETA: 1s - loss: 0.0286 - acc: 0.9902 - ETA: 1s - loss: 0.0281 - acc: 0.9904 - ETA: 1s - loss: 0.0278 - acc: 0.9906 - ETA: 1s - loss: 0.0283 - acc: 0.9905 - ETA: 1s - loss: 0.0285 - acc: 0.9904 - ETA: 1s - loss: 0.0284 - acc: 0.9904 - ETA: 1s - loss: 0.0279 - acc: 0.9906 - ETA: 1s - loss: 0.0289 - acc: 0.9903 - ETA: 1s - loss: 0.0296 - acc: 0.9898 - ETA: 1s - loss: 0.0292 - acc: 0.9900 - ETA: 1s - loss: 0.0292 - acc: 0.9900 - ETA: 1s - loss: 0.0317 - acc: 0.9897 - ETA: 1s - loss: 0.0314 - acc: 0.9898 - ETA: 1s - loss: 0.0312 - acc: 0.9900 - ETA: 1s - loss: 0.0318 - acc: 0.9896 - ETA: 0s - loss: 0.0333 - acc: 0.9894 - ETA: 0s - loss: 0.0334 - acc: 0.9890 - ETA: 0s - loss: 0.0346 - acc: 0.9886 - ETA: 0s - loss: 0.0346 - acc: 0.9884 - ETA: 0s - loss: 0.0352 - acc: 0.9882 - ETA: 0s - loss: 0.0350 - acc: 0.9882 - ETA: 0s - loss: 0.0349 - acc: 0.9882 - ETA: 0s - loss: 0.0347 - acc: 0.9883 - ETA: 0s - loss: 0.0344 - acc: 0.9885 - ETA: 0s - loss: 0.0346 - acc: 0.9885 - ETA: 0s - loss: 0.0349 - acc: 0.9883 - ETA: 0s - loss: 0.0345 - acc: 0.9885 - ETA: 0s - loss: 0.0346 - acc: 0.9884 - ETA: 0s - loss: 0.0344 - acc: 0.9884 - ETA: 0s - loss: 0.0341 - acc: 0.9885 - ETA: 0s - loss: 0.0340 - acc: 0.9885 - ETA: 0s - loss: 0.0339 - acc: 0.9885Epoch 00018: val_loss did not improve 6680/6680 [==============================] - 5s - loss: 0.0339 - acc: 0.9885 - val_loss: 0.9198 - val_acc: 0.8491 Epoch 20/20 6660/6680 [============================>.] - ETA: 4s - loss: 0.0630 - acc: 0.9500 - ETA: 4s - loss: 0.0221 - acc: 0.9900 - ETA: 4s - loss: 0.0650 - acc: 0.9889 - ETA: 4s - loss: 0.0548 - acc: 0.9885 - ETA: 4s - loss: 0.0480 - acc: 0.9882 - ETA: 4s - loss: 0.0466 - acc: 0.9881 - ETA: 4s - loss: 0.0411 - acc: 0.9900 - ETA: 4s - loss: 0.0363 - acc: 0.9914 - ETA: 4s - loss: 0.0345 - acc: 0.9906 - ETA: 4s - loss: 0.0310 - acc: 0.9917 - ETA: 4s - loss: 0.0285 - acc: 0.9925 - ETA: 4s - loss: 0.0277 - acc: 0.9920 - ETA: 4s - loss: 0.0269 - acc: 0.9917 - ETA: 4s - loss: 0.0251 - acc: 0.9923 - ETA: 4s - loss: 0.0249 - acc: 0.9911 - ETA: 3s - loss: 0.0245 - acc: 0.9908 - ETA: 3s - loss: 0.0235 - acc: 0.9914 - ETA: 3s - loss: 0.0232 - acc: 0.9912 - ETA: 3s - loss: 0.0274 - acc: 0.9910 - ETA: 3s - loss: 0.0295 - acc: 0.9908 - ETA: 3s - loss: 0.0286 - acc: 0.9912 - ETA: 3s - loss: 0.0281 - acc: 0.9917 - ETA: 3s - loss: 0.0271 - acc: 0.9920 - ETA: 3s - loss: 0.0263 - acc: 0.9924 - ETA: 3s - loss: 0.0257 - acc: 0.9927 - ETA: 3s - loss: 0.0253 - acc: 0.9925 - ETA: 3s - loss: 0.0267 - acc: 0.9918 - ETA: 3s - loss: 0.0263 - acc: 0.9921 - ETA: 3s - loss: 0.0255 - acc: 0.9924 - ETA: 3s - loss: 0.0259 - acc: 0.9922 - ETA: 3s - loss: 0.0254 - acc: 0.9925 - ETA: 3s - loss: 0.0247 - acc: 0.9927 - ETA: 2s - loss: 0.0247 - acc: 0.9926 - ETA: 2s - loss: 0.0243 - acc: 0.9928 - ETA: 2s - loss: 0.0241 - acc: 0.9930 - ETA: 2s - loss: 0.0237 - acc: 0.9932 - ETA: 2s - loss: 0.0239 - acc: 0.9931 - ETA: 2s - loss: 0.0238 - acc: 0.9932 - ETA: 2s - loss: 0.0243 - acc: 0.9931 - ETA: 2s - loss: 0.0262 - acc: 0.9926 - ETA: 2s - loss: 0.0259 - acc: 0.9925 - ETA: 2s - loss: 0.0264 - acc: 0.9924 - ETA: 2s - loss: 0.0258 - acc: 0.9926 - ETA: 2s - loss: 0.0254 - acc: 0.9927 - ETA: 2s - loss: 0.0269 - acc: 0.9926 - ETA: 2s - loss: 0.0301 - acc: 0.9919 - ETA: 2s - loss: 0.0300 - acc: 0.9918 - ETA: 2s - loss: 0.0294 - acc: 0.9920 - ETA: 2s - loss: 0.0289 - acc: 0.9922 - ETA: 1s - loss: 0.0288 - acc: 0.9921 - ETA: 1s - loss: 0.0296 - acc: 0.9920 - ETA: 1s - loss: 0.0317 - acc: 0.9919 - ETA: 1s - loss: 0.0316 - acc: 0.9918 - ETA: 1s - loss: 0.0314 - acc: 0.9920 - ETA: 1s - loss: 0.0316 - acc: 0.9917 - ETA: 1s - loss: 0.0312 - acc: 0.9918 - ETA: 1s - loss: 0.0310 - acc: 0.9917 - ETA: 1s - loss: 0.0310 - acc: 0.9917 - ETA: 1s - loss: 0.0314 - acc: 0.9914 - ETA: 1s - loss: 0.0312 - acc: 0.9913 - ETA: 1s - loss: 0.0312 - acc: 0.9912 - ETA: 1s - loss: 0.0312 - acc: 0.9914 - ETA: 1s - loss: 0.0308 - acc: 0.9915 - ETA: 1s - loss: 0.0305 - acc: 0.9914 - ETA: 1s - loss: 0.0318 - acc: 0.9912 - ETA: 1s - loss: 0.0315 - acc: 0.9913 - ETA: 1s - loss: 0.0312 - acc: 0.9914 - ETA: 0s - loss: 0.0312 - acc: 0.9914 - ETA: 0s - loss: 0.0317 - acc: 0.9913 - ETA: 0s - loss: 0.0324 - acc: 0.9911 - ETA: 0s - loss: 0.0320 - acc: 0.9912 - ETA: 0s - loss: 0.0324 - acc: 0.9910 - ETA: 0s - loss: 0.0322 - acc: 0.9909 - ETA: 0s - loss: 0.0318 - acc: 0.9911 - ETA: 0s - loss: 0.0328 - acc: 0.9908 - ETA: 0s - loss: 0.0326 - acc: 0.9908 - ETA: 0s - loss: 0.0328 - acc: 0.9907 - ETA: 0s - loss: 0.0328 - acc: 0.9907 - ETA: 0s - loss: 0.0325 - acc: 0.9908 - ETA: 0s - loss: 0.0321 - acc: 0.9909 - ETA: 0s - loss: 0.0320 - acc: 0.9909 - ETA: 0s - loss: 0.0317 - acc: 0.9910 - ETA: 0s - loss: 0.0314 - acc: 0.9911 - ETA: 0s - loss: 0.0314 - acc: 0.9910 - ETA: 0s - loss: 0.0311 - acc: 0.9911Epoch 00019: val_loss did not improve 6680/6680 [==============================] - 5s - loss: 0.0311 - acc: 0.9912 - val_loss: 0.9812 - val_acc: 0.8551 ---I am done saving model valid_InceptionV3 ----
### TODO: Train the model.
checkpointer_Xception = ModelCheckpoint(filepath='weights.best.Xception.hdf5',
verbose=1, save_best_only=True)
Xception_model.fit(train_Xception, train_targets,
validation_data=(valid_Xception, valid_targets),
epochs=20, batch_size=20, callbacks=[checkpointer_Xception], verbose=1)
print('---I am done saving model valid_Xception ----')
Train on 6680 samples, validate on 835 samples Epoch 1/20 6620/6680 [============================>.] - ETA: 274s - loss: 4.8607 - acc: 0.0500 - ETA: 97s - loss: 4.9696 - acc: 0.0333 - ETA: 61s - loss: 4.9393 - acc: 0.0700 - ETA: 53s - loss: 4.8943 - acc: 0.0750 - ETA: 42s - loss: 4.7621 - acc: 0.1000 - ETA: 35s - loss: 4.7242 - acc: 0.1100 - ETA: 31s - loss: 4.6249 - acc: 0.1250 - ETA: 27s - loss: 4.5341 - acc: 0.1536 - ETA: 25s - loss: 4.4944 - acc: 0.1719 - ETA: 23s - loss: 4.3514 - acc: 0.1972 - ETA: 21s - loss: 4.2249 - acc: 0.2200 - ETA: 20s - loss: 4.1353 - acc: 0.2318 - ETA: 19s - loss: 4.0066 - acc: 0.2479 - ETA: 18s - loss: 3.9159 - acc: 0.2558 - ETA: 17s - loss: 3.8220 - acc: 0.2696 - ETA: 16s - loss: 3.6664 - acc: 0.2919 - ETA: 16s - loss: 3.5555 - acc: 0.3121 - ETA: 15s - loss: 3.4820 - acc: 0.3229 - ETA: 15s - loss: 3.3946 - acc: 0.3405 - ETA: 14s - loss: 3.3260 - acc: 0.3513 - ETA: 14s - loss: 3.2398 - acc: 0.3671 - ETA: 14s - loss: 3.1632 - acc: 0.3826 - ETA: 13s - loss: 3.0538 - acc: 0.4022 - ETA: 13s - loss: 3.0241 - acc: 0.4064 - ETA: 13s - loss: 2.9682 - acc: 0.4173 - ETA: 13s - loss: 2.9049 - acc: 0.4225 - ETA: 12s - loss: 2.8516 - acc: 0.4321 - ETA: 12s - loss: 2.7875 - acc: 0.4482 - ETA: 12s - loss: 2.7076 - acc: 0.4603 - ETA: 12s - loss: 2.6659 - acc: 0.4642 - ETA: 11s - loss: 2.6361 - acc: 0.4694 - ETA: 11s - loss: 2.5742 - acc: 0.4800 - ETA: 11s - loss: 2.5362 - acc: 0.4866 - ETA: 11s - loss: 2.5182 - acc: 0.4890 - ETA: 11s - loss: 2.4822 - acc: 0.4943 - ETA: 10s - loss: 2.4440 - acc: 0.5014 - ETA: 10s - loss: 2.3999 - acc: 0.5081 - ETA: 10s - loss: 2.3604 - acc: 0.5138 - ETA: 10s - loss: 2.3257 - acc: 0.5186 - ETA: 10s - loss: 2.2907 - acc: 0.5244 - ETA: 9s - loss: 2.2390 - acc: 0.5337 - ETA: 9s - loss: 2.2109 - acc: 0.5382 - ETA: 9s - loss: 2.1810 - acc: 0.5437 - ETA: 9s - loss: 2.1493 - acc: 0.5494 - ETA: 9s - loss: 2.1225 - acc: 0.5544 - ETA: 9s - loss: 2.1082 - acc: 0.5571 - ETA: 9s - loss: 2.0899 - acc: 0.5596 - ETA: 9s - loss: 2.0735 - acc: 0.5594 - ETA: 8s - loss: 2.0363 - acc: 0.5662 - ETA: 8s - loss: 2.0101 - acc: 0.5708 - ETA: 8s - loss: 1.9849 - acc: 0.5748 - ETA: 8s - loss: 1.9637 - acc: 0.5776 - ETA: 8s - loss: 1.9436 - acc: 0.5799 - ETA: 8s - loss: 1.9221 - acc: 0.5830 - ETA: 8s - loss: 1.8895 - acc: 0.5893 - ETA: 8s - loss: 1.8698 - acc: 0.5925 - ETA: 7s - loss: 1.8429 - acc: 0.5979 - ETA: 7s - loss: 1.8223 - acc: 0.6017 - ETA: 7s - loss: 1.8038 - acc: 0.6037 - ETA: 7s - loss: 1.7882 - acc: 0.6061 - ETA: 7s - loss: 1.7606 - acc: 0.6115 - ETA: 7s - loss: 1.7347 - acc: 0.6163 - ETA: 7s - loss: 1.7214 - acc: 0.6187 - ETA: 7s - loss: 1.7083 - acc: 0.6207 - ETA: 7s - loss: 1.7021 - acc: 0.6216 - ETA: 7s - loss: 1.6871 - acc: 0.6250 - ETA: 7s - loss: 1.6775 - acc: 0.6264 - ETA: 6s - loss: 1.6635 - acc: 0.6293 - ETA: 6s - loss: 1.6498 - acc: 0.6317 - ETA: 6s - loss: 1.6369 - acc: 0.6337 - ETA: 6s - loss: 1.6203 - acc: 0.6370 - ETA: 6s - loss: 1.6062 - acc: 0.6399 - ETA: 6s - loss: 1.5941 - acc: 0.6420 - ETA: 6s - loss: 1.5761 - acc: 0.6451 - ETA: 6s - loss: 1.5662 - acc: 0.6468 - ETA: 6s - loss: 1.5607 - acc: 0.6474 - ETA: 6s - loss: 1.5501 - acc: 0.6487 - ETA: 6s - loss: 1.5428 - acc: 0.6491 - ETA: 6s - loss: 1.5351 - acc: 0.6500 - ETA: 5s - loss: 1.5263 - acc: 0.6509 - ETA: 5s - loss: 1.5161 - acc: 0.6527 - ETA: 5s - loss: 1.5071 - acc: 0.6545 - ETA: 5s - loss: 1.5023 - acc: 0.6550 - ETA: 5s - loss: 1.4914 - acc: 0.6570 - ETA: 5s - loss: 1.4806 - acc: 0.6592 - ETA: 5s - loss: 1.4681 - acc: 0.6624 - ETA: 5s - loss: 1.4583 - acc: 0.6642 - ETA: 5s - loss: 1.4453 - acc: 0.6668 - ETA: 5s - loss: 1.4366 - acc: 0.6688 - ETA: 4s - loss: 1.4255 - acc: 0.6712 - ETA: 4s - loss: 1.4133 - acc: 0.6733 - ETA: 4s - loss: 1.4100 - acc: 0.6734 - ETA: 4s - loss: 1.4062 - acc: 0.6741 - ETA: 4s - loss: 1.4008 - acc: 0.6753 - ETA: 4s - loss: 1.3920 - acc: 0.6768 - ETA: 4s - loss: 1.3851 - acc: 0.6776 - ETA: 4s - loss: 1.3791 - acc: 0.6788 - ETA: 4s - loss: 1.3690 - acc: 0.6813 - ETA: 4s - loss: 1.3630 - acc: 0.6824 - ETA: 4s - loss: 1.3540 - acc: 0.6841 - ETA: 4s - loss: 1.3468 - acc: 0.6847 - ETA: 4s - loss: 1.3386 - acc: 0.6863 - ETA: 4s - loss: 1.3305 - acc: 0.6867 - ETA: 4s - loss: 1.3185 - acc: 0.6885 - ETA: 3s - loss: 1.3086 - acc: 0.6905 - ETA: 3s - loss: 1.3009 - acc: 0.6922 - ETA: 3s - loss: 1.2963 - acc: 0.6932 - ETA: 3s - loss: 1.2907 - acc: 0.6932 - ETA: 3s - loss: 1.2831 - acc: 0.6951 - ETA: 3s - loss: 1.2799 - acc: 0.6962 - ETA: 3s - loss: 1.2765 - acc: 0.6974 - ETA: 3s - loss: 1.2711 - acc: 0.6983 - ETA: 3s - loss: 1.2639 - acc: 0.6998 - ETA: 3s - loss: 1.2579 - acc: 0.7002 - ETA: 3s - loss: 1.2573 - acc: 0.7002 - ETA: 3s - loss: 1.2507 - acc: 0.7015 - ETA: 3s - loss: 1.2453 - acc: 0.7025 - ETA: 3s - loss: 1.2400 - acc: 0.7035 - ETA: 3s - loss: 1.2360 - acc: 0.7041 - ETA: 2s - loss: 1.2305 - acc: 0.7053 - ETA: 2s - loss: 1.2230 - acc: 0.7071 - ETA: 2s - loss: 1.2167 - acc: 0.7084 - ETA: 2s - loss: 1.2109 - acc: 0.7096 - ETA: 2s - loss: 1.2048 - acc: 0.7109 - ETA: 2s - loss: 1.1964 - acc: 0.7127 - ETA: 2s - loss: 1.1944 - acc: 0.7128 - ETA: 2s - loss: 1.1881 - acc: 0.7143 - ETA: 2s - loss: 1.1828 - acc: 0.7151 - ETA: 2s - loss: 1.1801 - acc: 0.7150 - ETA: 2s - loss: 1.1754 - acc: 0.7158 - ETA: 2s - loss: 1.1713 - acc: 0.7165 - ETA: 2s - loss: 1.1655 - acc: 0.7177 - ETA: 2s - loss: 1.1582 - acc: 0.7186 - ETA: 2s - loss: 1.1550 - acc: 0.7192 - ETA: 1s - loss: 1.1514 - acc: 0.7200 - ETA: 1s - loss: 1.1474 - acc: 0.7208 - ETA: 1s - loss: 1.1460 - acc: 0.7206 - ETA: 1s - loss: 1.1425 - acc: 0.7210 - ETA: 1s - loss: 1.1366 - acc: 0.7224 - ETA: 1s - loss: 1.1349 - acc: 0.7225 - ETA: 1s - loss: 1.1294 - acc: 0.7233 - ETA: 1s - loss: 1.1258 - acc: 0.7240 - ETA: 1s - loss: 1.1239 - acc: 0.7245 - ETA: 1s - loss: 1.1210 - acc: 0.7253 - ETA: 1s - loss: 1.1209 - acc: 0.7255 - ETA: 1s - loss: 1.1174 - acc: 0.7257 - ETA: 1s - loss: 1.1125 - acc: 0.7270 - ETA: 1s - loss: 1.1077 - acc: 0.7275 - ETA: 1s - loss: 1.1030 - acc: 0.7285 - ETA: 1s - loss: 1.0988 - acc: 0.7293 - ETA: 0s - loss: 1.0965 - acc: 0.7297 - ETA: 0s - loss: 1.0921 - acc: 0.7304 - ETA: 0s - loss: 1.0877 - acc: 0.7311 - ETA: 0s - loss: 1.0833 - acc: 0.7321 - ETA: 0s - loss: 1.0787 - acc: 0.7330 - ETA: 0s - loss: 1.0759 - acc: 0.7339 - ETA: 0s - loss: 1.0699 - acc: 0.7350 - ETA: 0s - loss: 1.0669 - acc: 0.7354 - ETA: 0s - loss: 1.0652 - acc: 0.7358 - ETA: 0s - loss: 1.0638 - acc: 0.7357 - ETA: 0s - loss: 1.0611 - acc: 0.7359 - ETA: 0s - loss: 1.0581 - acc: 0.7363 - ETA: 0s - loss: 1.0537 - acc: 0.7373Epoch 00000: val_loss improved from inf to 0.51751, saving model to weights.best.Xception.hdf5 6680/6680 [==============================] - 11s - loss: 1.0489 - acc: 0.7386 - val_loss: 0.5175 - val_acc: 0.8287 Epoch 2/20 6620/6680 [============================>.] - ETA: 7s - loss: 0.3979 - acc: 0.8500 - ETA: 7s - loss: 0.3478 - acc: 0.8500 - ETA: 7s - loss: 0.3562 - acc: 0.8714 - ETA: 7s - loss: 0.3885 - acc: 0.8650 - ETA: 7s - loss: 0.3679 - acc: 0.8692 - ETA: 7s - loss: 0.3545 - acc: 0.8719 - ETA: 7s - loss: 0.3575 - acc: 0.8737 - ETA: 6s - loss: 0.3531 - acc: 0.8750 - ETA: 6s - loss: 0.3631 - acc: 0.8720 - ETA: 6s - loss: 0.3674 - acc: 0.8750 - ETA: 6s - loss: 0.3743 - acc: 0.8742 - ETA: 6s - loss: 0.3755 - acc: 0.8750 - ETA: 6s - loss: 0.3889 - acc: 0.8757 - ETA: 6s - loss: 0.3845 - acc: 0.8787 - ETA: 6s - loss: 0.3848 - acc: 0.8791 - ETA: 6s - loss: 0.3822 - acc: 0.8783 - ETA: 6s - loss: 0.3915 - acc: 0.8765 - ETA: 6s - loss: 0.3991 - acc: 0.8712 - ETA: 6s - loss: 0.4072 - acc: 0.8673 - ETA: 6s - loss: 0.3966 - acc: 0.8724 - ETA: 6s - loss: 0.4024 - acc: 0.8689 - ETA: 5s - loss: 0.4074 - acc: 0.8672 - ETA: 5s - loss: 0.4160 - acc: 0.8634 - ETA: 5s - loss: 0.4107 - acc: 0.8657 - ETA: 5s - loss: 0.4102 - acc: 0.8651 - ETA: 5s - loss: 0.4076 - acc: 0.8651 - ETA: 5s - loss: 0.4050 - acc: 0.8658 - ETA: 5s - loss: 0.4067 - acc: 0.8652 - ETA: 5s - loss: 0.3986 - acc: 0.8682 - ETA: 5s - loss: 0.4027 - acc: 0.8665 - ETA: 5s - loss: 0.3969 - acc: 0.8681 - ETA: 5s - loss: 0.3979 - acc: 0.8681 - ETA: 5s - loss: 0.3987 - acc: 0.8675 - ETA: 5s - loss: 0.4006 - acc: 0.8675 - ETA: 5s - loss: 0.4008 - acc: 0.8675 - ETA: 5s - loss: 0.4022 - acc: 0.8665 - ETA: 4s - loss: 0.4065 - acc: 0.8665 - ETA: 4s - loss: 0.4075 - acc: 0.8674 - ETA: 4s - loss: 0.4108 - acc: 0.8665 - ETA: 4s - loss: 0.4084 - acc: 0.8682 - ETA: 4s - loss: 0.4085 - acc: 0.8690 - ETA: 4s - loss: 0.4053 - acc: 0.8698 - ETA: 4s - loss: 0.3998 - acc: 0.8717 - ETA: 4s - loss: 0.4016 - acc: 0.8708 - ETA: 4s - loss: 0.4003 - acc: 0.8722 - ETA: 4s - loss: 0.3987 - acc: 0.8721 - ETA: 4s - loss: 0.3959 - acc: 0.8730 - ETA: 4s - loss: 0.4032 - acc: 0.8718 - ETA: 4s - loss: 0.4025 - acc: 0.8731 - ETA: 4s - loss: 0.4017 - acc: 0.8736 - ETA: 4s - loss: 0.4002 - acc: 0.8748 - ETA: 3s - loss: 0.4011 - acc: 0.8744 - ETA: 3s - loss: 0.4079 - acc: 0.8729 - ETA: 3s - loss: 0.4070 - acc: 0.8725 - ETA: 3s - loss: 0.4030 - acc: 0.8736 - ETA: 3s - loss: 0.4059 - acc: 0.8735 - ETA: 3s - loss: 0.4022 - acc: 0.8749 - ETA: 3s - loss: 0.4008 - acc: 0.8747 - ETA: 3s - loss: 0.3980 - acc: 0.8749 - ETA: 3s - loss: 0.4005 - acc: 0.8739 - ETA: 3s - loss: 0.4033 - acc: 0.8735 - ETA: 3s - loss: 0.4025 - acc: 0.8734 - ETA: 3s - loss: 0.4028 - acc: 0.8738 - ETA: 3s - loss: 0.4013 - acc: 0.8739 - ETA: 3s - loss: 0.4014 - acc: 0.8744 - ETA: 3s - loss: 0.4039 - acc: 0.8737 - ETA: 2s - loss: 0.4063 - acc: 0.8736 - ETA: 2s - loss: 0.4062 - acc: 0.8738 - ETA: 2s - loss: 0.4063 - acc: 0.8741 - ETA: 2s - loss: 0.4065 - acc: 0.8743 - ETA: 2s - loss: 0.4064 - acc: 0.8737 - ETA: 2s - loss: 0.4060 - acc: 0.8745 - ETA: 2s - loss: 0.4054 - acc: 0.8744 - ETA: 2s - loss: 0.4108 - acc: 0.8727 - ETA: 2s - loss: 0.4100 - acc: 0.8733 - ETA: 2s - loss: 0.4084 - acc: 0.8741 - ETA: 2s - loss: 0.4072 - acc: 0.8747 - ETA: 2s - loss: 0.4082 - acc: 0.8746 - ETA: 2s - loss: 0.4084 - acc: 0.8740 - ETA: 2s - loss: 0.4073 - acc: 0.8748 - ETA: 2s - loss: 0.4061 - acc: 0.8747 - ETA: 1s - loss: 0.4082 - acc: 0.8746 - ETA: 1s - loss: 0.4086 - acc: 0.8743 - ETA: 1s - loss: 0.4104 - acc: 0.8736 - ETA: 1s - loss: 0.4101 - acc: 0.8737 - ETA: 1s - loss: 0.4070 - acc: 0.8746 - ETA: 1s - loss: 0.4047 - acc: 0.8753 - ETA: 1s - loss: 0.4035 - acc: 0.8754 - ETA: 1s - loss: 0.4038 - acc: 0.8755 - ETA: 1s - loss: 0.4050 - acc: 0.8743 - ETA: 1s - loss: 0.4059 - acc: 0.8740 - ETA: 1s - loss: 0.4074 - acc: 0.8735 - ETA: 1s - loss: 0.4077 - acc: 0.8736 - ETA: 1s - loss: 0.4063 - acc: 0.8743 - ETA: 1s - loss: 0.4072 - acc: 0.8742 - ETA: 1s - loss: 0.4055 - acc: 0.8747 - ETA: 0s - loss: 0.4073 - acc: 0.8744 - ETA: 0s - loss: 0.4058 - acc: 0.8748 - ETA: 0s - loss: 0.4029 - acc: 0.8758 - ETA: 0s - loss: 0.4026 - acc: 0.8753 - ETA: 0s - loss: 0.4013 - acc: 0.8754 - ETA: 0s - loss: 0.3989 - acc: 0.8762 - ETA: 0s - loss: 0.3989 - acc: 0.8759 - ETA: 0s - loss: 0.3960 - acc: 0.8769 - ETA: 0s - loss: 0.3959 - acc: 0.8768 - ETA: 0s - loss: 0.3962 - acc: 0.8764 - ETA: 0s - loss: 0.3972 - acc: 0.8765 - ETA: 0s - loss: 0.3974 - acc: 0.8761 - ETA: 0s - loss: 0.3981 - acc: 0.8760 - ETA: 0s - loss: 0.3991 - acc: 0.8758 - ETA: 0s - loss: 0.3967 - acc: 0.8766Epoch 00001: val_loss improved from 0.51751 to 0.49419, saving model to weights.best.Xception.hdf5 6680/6680 [==============================] - 7s - loss: 0.3970 - acc: 0.8765 - val_loss: 0.4942 - val_acc: 0.8467 Epoch 3/20 6640/6680 [============================>.] - ETA: 6s - loss: 0.2117 - acc: 0.9500 - ETA: 6s - loss: 0.3529 - acc: 0.9125 - ETA: 6s - loss: 0.3027 - acc: 0.9000 - ETA: 6s - loss: 0.3290 - acc: 0.8950 - ETA: 6s - loss: 0.3522 - acc: 0.8885 - ETA: 6s - loss: 0.3138 - acc: 0.8969 - ETA: 6s - loss: 0.3289 - acc: 0.8974 - ETA: 6s - loss: 0.3111 - acc: 0.9023 - ETA: 6s - loss: 0.3087 - acc: 0.8960 - ETA: 6s - loss: 0.3035 - acc: 0.9000 - ETA: 6s - loss: 0.2889 - acc: 0.9048 - ETA: 6s - loss: 0.2840 - acc: 0.9088 - ETA: 6s - loss: 0.2783 - acc: 0.9095 - ETA: 6s - loss: 0.2810 - acc: 0.9087 - ETA: 6s - loss: 0.2698 - acc: 0.9128 - ETA: 6s - loss: 0.2898 - acc: 0.9065 - ETA: 6s - loss: 0.2967 - acc: 0.9051 - ETA: 6s - loss: 0.2896 - acc: 0.9067 - ETA: 5s - loss: 0.2954 - acc: 0.9045 - ETA: 5s - loss: 0.2979 - acc: 0.9052 - ETA: 5s - loss: 0.2950 - acc: 0.9074 - ETA: 5s - loss: 0.3010 - acc: 0.9086 - ETA: 5s - loss: 0.3164 - acc: 0.9037 - ETA: 5s - loss: 0.3164 - acc: 0.9043 - ETA: 5s - loss: 0.3249 - acc: 0.9027 - ETA: 5s - loss: 0.3204 - acc: 0.9033 - ETA: 5s - loss: 0.3175 - acc: 0.9032 - ETA: 5s - loss: 0.3323 - acc: 0.8988 - ETA: 5s - loss: 0.3274 - acc: 0.9000 - ETA: 5s - loss: 0.3212 - acc: 0.9017 - ETA: 5s - loss: 0.3189 - acc: 0.9016 - ETA: 5s - loss: 0.3228 - acc: 0.9000 - ETA: 5s - loss: 0.3217 - acc: 0.9005 - ETA: 5s - loss: 0.3211 - acc: 0.9015 - ETA: 4s - loss: 0.3208 - acc: 0.9019 - ETA: 4s - loss: 0.3238 - acc: 0.9005 - ETA: 4s - loss: 0.3209 - acc: 0.9014 - ETA: 4s - loss: 0.3240 - acc: 0.9009 - ETA: 4s - loss: 0.3246 - acc: 0.8996 - ETA: 4s - loss: 0.3243 - acc: 0.8996 - ETA: 4s - loss: 0.3244 - acc: 0.9004 - ETA: 4s - loss: 0.3211 - acc: 0.9016 - ETA: 4s - loss: 0.3182 - acc: 0.9024 - ETA: 4s - loss: 0.3195 - acc: 0.9019 - ETA: 4s - loss: 0.3174 - acc: 0.9019 - ETA: 4s - loss: 0.3152 - acc: 0.9022 - ETA: 4s - loss: 0.3161 - acc: 0.9022 - ETA: 4s - loss: 0.3182 - acc: 0.9028 - ETA: 4s - loss: 0.3187 - acc: 0.9017 - ETA: 4s - loss: 0.3183 - acc: 0.9024 - ETA: 3s - loss: 0.3141 - acc: 0.9037 - ETA: 3s - loss: 0.3120 - acc: 0.9046 - ETA: 3s - loss: 0.3084 - acc: 0.9058 - ETA: 3s - loss: 0.3059 - acc: 0.9063 - ETA: 3s - loss: 0.3081 - acc: 0.9052 - ETA: 3s - loss: 0.3085 - acc: 0.9045 - ETA: 3s - loss: 0.3092 - acc: 0.9045 - ETA: 3s - loss: 0.3124 - acc: 0.9035 - ETA: 3s - loss: 0.3151 - acc: 0.9032 - ETA: 3s - loss: 0.3136 - acc: 0.9034 - ETA: 3s - loss: 0.3147 - acc: 0.9033 - ETA: 3s - loss: 0.3166 - acc: 0.9036 - ETA: 3s - loss: 0.3173 - acc: 0.9032 - ETA: 3s - loss: 0.3161 - acc: 0.9032 - ETA: 3s - loss: 0.3191 - acc: 0.9018 - ETA: 2s - loss: 0.3205 - acc: 0.9015 - ETA: 2s - loss: 0.3223 - acc: 0.9015 - ETA: 2s - loss: 0.3191 - acc: 0.9027 - ETA: 2s - loss: 0.3212 - acc: 0.9027 - ETA: 2s - loss: 0.3245 - acc: 0.9014 - ETA: 2s - loss: 0.3275 - acc: 0.9002 - ETA: 2s - loss: 0.3277 - acc: 0.8998 - ETA: 2s - loss: 0.3308 - acc: 0.8991 - ETA: 2s - loss: 0.3283 - acc: 0.8995 - ETA: 2s - loss: 0.3290 - acc: 0.8991 - ETA: 2s - loss: 0.3277 - acc: 0.8993 - ETA: 2s - loss: 0.3260 - acc: 0.8998 - ETA: 2s - loss: 0.3253 - acc: 0.8994 - ETA: 2s - loss: 0.3238 - acc: 0.8998 - ETA: 2s - loss: 0.3236 - acc: 0.9000 - ETA: 2s - loss: 0.3244 - acc: 0.8996 - ETA: 1s - loss: 0.3254 - acc: 0.8981 - ETA: 1s - loss: 0.3250 - acc: 0.8980 - ETA: 1s - loss: 0.3265 - acc: 0.8972 - ETA: 1s - loss: 0.3280 - acc: 0.8972 - ETA: 1s - loss: 0.3287 - acc: 0.8967 - ETA: 1s - loss: 0.3274 - acc: 0.8971 - ETA: 1s - loss: 0.3265 - acc: 0.8977 - ETA: 1s - loss: 0.3260 - acc: 0.8979 - ETA: 1s - loss: 0.3251 - acc: 0.8981 - ETA: 1s - loss: 0.3229 - acc: 0.8987 - ETA: 1s - loss: 0.3214 - acc: 0.8991 - ETA: 1s - loss: 0.3217 - acc: 0.8989 - ETA: 1s - loss: 0.3212 - acc: 0.8987 - ETA: 1s - loss: 0.3232 - acc: 0.8982 - ETA: 1s - loss: 0.3216 - acc: 0.8984 - ETA: 0s - loss: 0.3191 - acc: 0.8993 - ETA: 0s - loss: 0.3200 - acc: 0.8993 - ETA: 0s - loss: 0.3210 - acc: 0.8991 - ETA: 0s - loss: 0.3224 - acc: 0.8987 - ETA: 0s - loss: 0.3209 - acc: 0.8992 - ETA: 0s - loss: 0.3225 - acc: 0.8988 - ETA: 0s - loss: 0.3248 - acc: 0.8989 - ETA: 0s - loss: 0.3239 - acc: 0.8990 - ETA: 0s - loss: 0.3236 - acc: 0.8992 - ETA: 0s - loss: 0.3232 - acc: 0.8995 - ETA: 0s - loss: 0.3214 - acc: 0.9002 - ETA: 0s - loss: 0.3219 - acc: 0.9005 - ETA: 0s - loss: 0.3239 - acc: 0.8998 - ETA: 0s - loss: 0.3249 - acc: 0.8995 - ETA: 0s - loss: 0.3240 - acc: 0.8997 - ETA: 0s - loss: 0.3232 - acc: 0.9000Epoch 00002: val_loss improved from 0.49419 to 0.48968, saving model to weights.best.Xception.hdf5 6680/6680 [==============================] - 7s - loss: 0.3235 - acc: 0.8999 - val_loss: 0.4897 - val_acc: 0.8455 Epoch 4/20 6640/6680 [============================>.] - ETA: 6s - loss: 0.2434 - acc: 0.8500 - ETA: 6s - loss: 0.2229 - acc: 0.9000 - ETA: 6s - loss: 0.2553 - acc: 0.9071 - ETA: 6s - loss: 0.2461 - acc: 0.9100 - ETA: 6s - loss: 0.2156 - acc: 0.9231 - ETA: 6s - loss: 0.2247 - acc: 0.9187 - ETA: 6s - loss: 0.2415 - acc: 0.9105 - ETA: 6s - loss: 0.2547 - acc: 0.9114 - ETA: 6s - loss: 0.2454 - acc: 0.9180 - ETA: 6s - loss: 0.2304 - acc: 0.9214 - ETA: 6s - loss: 0.2393 - acc: 0.9145 - ETA: 6s - loss: 0.2477 - acc: 0.9147 - ETA: 6s - loss: 0.2553 - acc: 0.9135 - ETA: 6s - loss: 0.2492 - acc: 0.9162 - ETA: 6s - loss: 0.2475 - acc: 0.9174 - ETA: 6s - loss: 0.2441 - acc: 0.9196 - ETA: 6s - loss: 0.2489 - acc: 0.9194 - ETA: 6s - loss: 0.2410 - acc: 0.9221 - ETA: 5s - loss: 0.2414 - acc: 0.9218 - ETA: 5s - loss: 0.2383 - acc: 0.9207 - ETA: 5s - loss: 0.2422 - acc: 0.9180 - ETA: 5s - loss: 0.2422 - acc: 0.9180 - ETA: 5s - loss: 0.2392 - acc: 0.9194 - ETA: 5s - loss: 0.2348 - acc: 0.9207 - ETA: 5s - loss: 0.2367 - acc: 0.9199 - ETA: 5s - loss: 0.2391 - acc: 0.9197 - ETA: 5s - loss: 0.2416 - acc: 0.9196 - ETA: 5s - loss: 0.2449 - acc: 0.9195 - ETA: 5s - loss: 0.2495 - acc: 0.9176 - ETA: 5s - loss: 0.2430 - acc: 0.9205 - ETA: 5s - loss: 0.2404 - acc: 0.9203 - ETA: 5s - loss: 0.2412 - acc: 0.9210 - ETA: 5s - loss: 0.2425 - acc: 0.9198 - ETA: 5s - loss: 0.2452 - acc: 0.9187 - ETA: 5s - loss: 0.2435 - acc: 0.9191 - ETA: 4s - loss: 0.2430 - acc: 0.9190 - ETA: 4s - loss: 0.2434 - acc: 0.9190 - ETA: 4s - loss: 0.2427 - acc: 0.9194 - ETA: 4s - loss: 0.2431 - acc: 0.9197 - ETA: 4s - loss: 0.2405 - acc: 0.9209 - ETA: 4s - loss: 0.2395 - acc: 0.9221 - ETA: 4s - loss: 0.2396 - acc: 0.9220 - ETA: 4s - loss: 0.2384 - acc: 0.9222 - ETA: 4s - loss: 0.2361 - acc: 0.9236 - ETA: 4s - loss: 0.2375 - acc: 0.9223 - ETA: 4s - loss: 0.2368 - acc: 0.9226 - ETA: 4s - loss: 0.2382 - acc: 0.9225 - ETA: 4s - loss: 0.2404 - acc: 0.9223 - ETA: 4s - loss: 0.2402 - acc: 0.9222 - ETA: 4s - loss: 0.2412 - acc: 0.9218 - ETA: 3s - loss: 0.2459 - acc: 0.9210 - ETA: 3s - loss: 0.2496 - acc: 0.9199 - ETA: 3s - loss: 0.2568 - acc: 0.9183 - ETA: 3s - loss: 0.2542 - acc: 0.9192 - ETA: 3s - loss: 0.2585 - acc: 0.9176 - ETA: 3s - loss: 0.2587 - acc: 0.9176 - ETA: 3s - loss: 0.2577 - acc: 0.9182 - ETA: 3s - loss: 0.2570 - acc: 0.9184 - ETA: 3s - loss: 0.2584 - acc: 0.9172 - ETA: 3s - loss: 0.2579 - acc: 0.9169 - ETA: 3s - loss: 0.2562 - acc: 0.9175 - ETA: 3s - loss: 0.2573 - acc: 0.9175 - ETA: 3s - loss: 0.2562 - acc: 0.9177 - ETA: 3s - loss: 0.2560 - acc: 0.9175 - ETA: 3s - loss: 0.2544 - acc: 0.9180 - ETA: 2s - loss: 0.2543 - acc: 0.9182 - ETA: 2s - loss: 0.2546 - acc: 0.9184 - ETA: 2s - loss: 0.2533 - acc: 0.9184 - ETA: 2s - loss: 0.2549 - acc: 0.9179 - ETA: 2s - loss: 0.2552 - acc: 0.9181 - ETA: 2s - loss: 0.2580 - acc: 0.9174 - ETA: 2s - loss: 0.2575 - acc: 0.9171 - ETA: 2s - loss: 0.2584 - acc: 0.9167 - ETA: 2s - loss: 0.2579 - acc: 0.9169 - ETA: 2s - loss: 0.2596 - acc: 0.9167 - ETA: 2s - loss: 0.2593 - acc: 0.9164 - ETA: 2s - loss: 0.2580 - acc: 0.9171 - ETA: 2s - loss: 0.2602 - acc: 0.9169 - ETA: 2s - loss: 0.2636 - acc: 0.9162 - ETA: 2s - loss: 0.2638 - acc: 0.9160 - ETA: 2s - loss: 0.2617 - acc: 0.9167 - ETA: 1s - loss: 0.2621 - acc: 0.9167 - ETA: 1s - loss: 0.2663 - acc: 0.9154 - ETA: 1s - loss: 0.2714 - acc: 0.9151 - ETA: 1s - loss: 0.2731 - acc: 0.9145 - ETA: 1s - loss: 0.2749 - acc: 0.9147 - ETA: 1s - loss: 0.2756 - acc: 0.9141 - ETA: 1s - loss: 0.2773 - acc: 0.9132 - ETA: 1s - loss: 0.2761 - acc: 0.9134 - ETA: 1s - loss: 0.2749 - acc: 0.9142 - ETA: 1s - loss: 0.2754 - acc: 0.9139 - ETA: 1s - loss: 0.2755 - acc: 0.9137 - ETA: 1s - loss: 0.2750 - acc: 0.9141 - ETA: 1s - loss: 0.2753 - acc: 0.9138 - ETA: 1s - loss: 0.2778 - acc: 0.9137 - ETA: 1s - loss: 0.2781 - acc: 0.9137 - ETA: 1s - loss: 0.2781 - acc: 0.9134 - ETA: 0s - loss: 0.2801 - acc: 0.9133 - ETA: 0s - loss: 0.2783 - acc: 0.9137 - ETA: 0s - loss: 0.2772 - acc: 0.9139 - ETA: 0s - loss: 0.2766 - acc: 0.9142 - ETA: 0s - loss: 0.2768 - acc: 0.9141 - ETA: 0s - loss: 0.2771 - acc: 0.9138 - ETA: 0s - loss: 0.2769 - acc: 0.9138 - ETA: 0s - loss: 0.2767 - acc: 0.9135 - ETA: 0s - loss: 0.2769 - acc: 0.9137 - ETA: 0s - loss: 0.2772 - acc: 0.9134 - ETA: 0s - loss: 0.2768 - acc: 0.9134 - ETA: 0s - loss: 0.2771 - acc: 0.9132 - ETA: 0s - loss: 0.2814 - acc: 0.9124 - ETA: 0s - loss: 0.2821 - acc: 0.9122 - ETA: 0s - loss: 0.2809 - acc: 0.9125Epoch 00003: val_loss did not improve 6680/6680 [==============================] - 7s - loss: 0.2805 - acc: 0.9124 - val_loss: 0.4960 - val_acc: 0.8551 Epoch 5/20 6660/6680 [============================>.] - ETA: 6s - loss: 0.2675 - acc: 0.9500 - ETA: 6s - loss: 0.2163 - acc: 0.9500 - ETA: 6s - loss: 0.1891 - acc: 0.9429 - ETA: 6s - loss: 0.1682 - acc: 0.9500 - ETA: 6s - loss: 0.1567 - acc: 0.9462 - ETA: 6s - loss: 0.1586 - acc: 0.9437 - ETA: 6s - loss: 0.1491 - acc: 0.9500 - ETA: 6s - loss: 0.1609 - acc: 0.9477 - ETA: 6s - loss: 0.1563 - acc: 0.9520 - ETA: 6s - loss: 0.1601 - acc: 0.9482 - ETA: 6s - loss: 0.1729 - acc: 0.9468 - ETA: 6s - loss: 0.1730 - acc: 0.9471 - ETA: 6s - loss: 0.1810 - acc: 0.9459 - ETA: 6s - loss: 0.1916 - acc: 0.9400 - ETA: 6s - loss: 0.1947 - acc: 0.9407 - ETA: 6s - loss: 0.1905 - acc: 0.9424 - ETA: 6s - loss: 0.2041 - acc: 0.9357 - ETA: 5s - loss: 0.2072 - acc: 0.9365 - ETA: 5s - loss: 0.2120 - acc: 0.9364 - ETA: 5s - loss: 0.2120 - acc: 0.9353 - ETA: 5s - loss: 0.2109 - acc: 0.9352 - ETA: 5s - loss: 0.2087 - acc: 0.9344 - ETA: 5s - loss: 0.2130 - acc: 0.9321 - ETA: 5s - loss: 0.2103 - acc: 0.9314 - ETA: 5s - loss: 0.2135 - acc: 0.9301 - ETA: 5s - loss: 0.2163 - acc: 0.9289 - ETA: 5s - loss: 0.2142 - acc: 0.9297 - ETA: 5s - loss: 0.2115 - acc: 0.9299 - ETA: 5s - loss: 0.2098 - acc: 0.9300 - ETA: 5s - loss: 0.2107 - acc: 0.9295 - ETA: 5s - loss: 0.2101 - acc: 0.9297 - ETA: 5s - loss: 0.2078 - acc: 0.9309 - ETA: 5s - loss: 0.2032 - acc: 0.9325 - ETA: 4s - loss: 0.2035 - acc: 0.9330 - ETA: 4s - loss: 0.2044 - acc: 0.9325 - ETA: 4s - loss: 0.2051 - acc: 0.9325 - ETA: 4s - loss: 0.2015 - acc: 0.9339 - ETA: 4s - loss: 0.2017 - acc: 0.9339 - ETA: 4s - loss: 0.2015 - acc: 0.9330 - ETA: 4s - loss: 0.1997 - acc: 0.9339 - ETA: 4s - loss: 0.1976 - acc: 0.9343 - ETA: 4s - loss: 0.1962 - acc: 0.9347 - ETA: 4s - loss: 0.2039 - acc: 0.9339 - ETA: 4s - loss: 0.2081 - acc: 0.9331 - ETA: 4s - loss: 0.2054 - acc: 0.9338 - ETA: 4s - loss: 0.2021 - acc: 0.9349 - ETA: 4s - loss: 0.1992 - acc: 0.9360 - ETA: 4s - loss: 0.2056 - acc: 0.9352 - ETA: 4s - loss: 0.2031 - acc: 0.9359 - ETA: 3s - loss: 0.2063 - acc: 0.9348 - ETA: 3s - loss: 0.2036 - acc: 0.9361 - ETA: 3s - loss: 0.2090 - acc: 0.9347 - ETA: 3s - loss: 0.2117 - acc: 0.9338 - ETA: 3s - loss: 0.2136 - acc: 0.9334 - ETA: 3s - loss: 0.2140 - acc: 0.9334 - ETA: 3s - loss: 0.2127 - acc: 0.9337 - ETA: 3s - loss: 0.2150 - acc: 0.9325 - ETA: 3s - loss: 0.2169 - acc: 0.9314 - ETA: 3s - loss: 0.2176 - acc: 0.9309 - ETA: 3s - loss: 0.2182 - acc: 0.9309 - ETA: 3s - loss: 0.2173 - acc: 0.9312 - ETA: 3s - loss: 0.2176 - acc: 0.9310 - ETA: 3s - loss: 0.2150 - acc: 0.9321 - ETA: 3s - loss: 0.2149 - acc: 0.9321 - ETA: 3s - loss: 0.2201 - acc: 0.9321 - ETA: 2s - loss: 0.2193 - acc: 0.9321 - ETA: 2s - loss: 0.2232 - acc: 0.9314 - ETA: 2s - loss: 0.2226 - acc: 0.9314 - ETA: 2s - loss: 0.2262 - acc: 0.9300 - ETA: 2s - loss: 0.2257 - acc: 0.9300 - ETA: 2s - loss: 0.2251 - acc: 0.9299 - ETA: 2s - loss: 0.2244 - acc: 0.9299 - ETA: 2s - loss: 0.2273 - acc: 0.9283 - ETA: 2s - loss: 0.2284 - acc: 0.9275 - ETA: 2s - loss: 0.2275 - acc: 0.9275 - ETA: 2s - loss: 0.2310 - acc: 0.9278 - ETA: 2s - loss: 0.2306 - acc: 0.9281 - ETA: 2s - loss: 0.2349 - acc: 0.9268 - ETA: 2s - loss: 0.2358 - acc: 0.9261 - ETA: 2s - loss: 0.2369 - acc: 0.9253 - ETA: 2s - loss: 0.2375 - acc: 0.9252 - ETA: 1s - loss: 0.2369 - acc: 0.9255 - ETA: 1s - loss: 0.2370 - acc: 0.9256 - ETA: 1s - loss: 0.2373 - acc: 0.9259 - ETA: 1s - loss: 0.2354 - acc: 0.9266 - ETA: 1s - loss: 0.2383 - acc: 0.9259 - ETA: 1s - loss: 0.2388 - acc: 0.9258 - ETA: 1s - loss: 0.2404 - acc: 0.9255 - ETA: 1s - loss: 0.2409 - acc: 0.9256 - ETA: 1s - loss: 0.2407 - acc: 0.9258 - ETA: 1s - loss: 0.2405 - acc: 0.9257 - ETA: 1s - loss: 0.2409 - acc: 0.9253 - ETA: 1s - loss: 0.2398 - acc: 0.9255 - ETA: 1s - loss: 0.2390 - acc: 0.9258 - ETA: 1s - loss: 0.2396 - acc: 0.9252 - ETA: 1s - loss: 0.2429 - acc: 0.9240 - ETA: 0s - loss: 0.2436 - acc: 0.9238 - ETA: 0s - loss: 0.2456 - acc: 0.9234 - ETA: 0s - loss: 0.2446 - acc: 0.9238 - ETA: 0s - loss: 0.2442 - acc: 0.9239 - ETA: 0s - loss: 0.2435 - acc: 0.9243 - ETA: 0s - loss: 0.2424 - acc: 0.9248 - ETA: 0s - loss: 0.2451 - acc: 0.9243 - ETA: 0s - loss: 0.2449 - acc: 0.9246 - ETA: 0s - loss: 0.2460 - acc: 0.9245 - ETA: 0s - loss: 0.2463 - acc: 0.9244 - ETA: 0s - loss: 0.2460 - acc: 0.9245 - ETA: 0s - loss: 0.2461 - acc: 0.9241 - ETA: 0s - loss: 0.2451 - acc: 0.9242 - ETA: 0s - loss: 0.2447 - acc: 0.9243 - ETA: 0s - loss: 0.2433 - acc: 0.9245 - ETA: 0s - loss: 0.2444 - acc: 0.9242Epoch 00004: val_loss improved from 0.48968 to 0.48411, saving model to weights.best.Xception.hdf5 6680/6680 [==============================] - 7s - loss: 0.2448 - acc: 0.9241 - val_loss: 0.4841 - val_acc: 0.8587 Epoch 6/20 6620/6680 [============================>.] - ETA: 6s - loss: 0.2373 - acc: 0.9000 - ETA: 6s - loss: 0.1525 - acc: 0.9500 - ETA: 6s - loss: 0.1618 - acc: 0.9429 - ETA: 6s - loss: 0.1618 - acc: 0.9500 - ETA: 6s - loss: 0.1528 - acc: 0.9538 - ETA: 6s - loss: 0.1455 - acc: 0.9500 - ETA: 6s - loss: 0.1410 - acc: 0.9500 - ETA: 6s - loss: 0.1423 - acc: 0.9523 - ETA: 6s - loss: 0.1553 - acc: 0.9480 - ETA: 6s - loss: 0.1771 - acc: 0.9446 - ETA: 6s - loss: 0.1784 - acc: 0.9419 - ETA: 6s - loss: 0.1705 - acc: 0.9441 - ETA: 6s - loss: 0.1732 - acc: 0.9432 - ETA: 6s - loss: 0.1697 - acc: 0.9437 - ETA: 6s - loss: 0.1740 - acc: 0.9419 - ETA: 6s - loss: 0.1750 - acc: 0.9391 - ETA: 6s - loss: 0.1713 - acc: 0.9408 - ETA: 6s - loss: 0.1653 - acc: 0.9423 - ETA: 6s - loss: 0.1715 - acc: 0.9400 - ETA: 5s - loss: 0.1749 - acc: 0.9397 - ETA: 5s - loss: 0.1718 - acc: 0.9402 - ETA: 5s - loss: 0.1775 - acc: 0.9406 - ETA: 5s - loss: 0.1760 - acc: 0.9410 - ETA: 5s - loss: 0.1788 - acc: 0.9393 - ETA: 5s - loss: 0.1767 - acc: 0.9397 - ETA: 5s - loss: 0.1743 - acc: 0.9408 - ETA: 5s - loss: 0.1750 - acc: 0.9405 - ETA: 5s - loss: 0.1717 - acc: 0.9415 - ETA: 5s - loss: 0.1726 - acc: 0.9406 - ETA: 5s - loss: 0.1742 - acc: 0.9398 - ETA: 5s - loss: 0.1725 - acc: 0.9396 - ETA: 5s - loss: 0.1771 - acc: 0.9383 - ETA: 5s - loss: 0.1797 - acc: 0.9381 - ETA: 4s - loss: 0.1782 - acc: 0.9395 - ETA: 4s - loss: 0.1777 - acc: 0.9393 - ETA: 4s - loss: 0.1756 - acc: 0.9401 - ETA: 4s - loss: 0.1766 - acc: 0.9408 - ETA: 4s - loss: 0.1770 - acc: 0.9402 - ETA: 4s - loss: 0.1788 - acc: 0.9409 - ETA: 4s - loss: 0.1787 - acc: 0.9407 - ETA: 4s - loss: 0.1839 - acc: 0.9384 - ETA: 4s - loss: 0.1910 - acc: 0.9379 - ETA: 4s - loss: 0.1882 - acc: 0.9386 - ETA: 4s - loss: 0.1940 - acc: 0.9377 - ETA: 4s - loss: 0.1944 - acc: 0.9380 - ETA: 4s - loss: 0.1934 - acc: 0.9379 - ETA: 4s - loss: 0.1944 - acc: 0.9378 - ETA: 4s - loss: 0.1934 - acc: 0.9380 - ETA: 4s - loss: 0.1937 - acc: 0.9372 - ETA: 3s - loss: 0.2007 - acc: 0.9361 - ETA: 3s - loss: 0.1987 - acc: 0.9368 - ETA: 3s - loss: 0.1980 - acc: 0.9370 - ETA: 3s - loss: 0.1966 - acc: 0.9369 - ETA: 3s - loss: 0.1982 - acc: 0.9366 - ETA: 3s - loss: 0.1982 - acc: 0.9365 - ETA: 3s - loss: 0.1972 - acc: 0.9370 - ETA: 3s - loss: 0.1961 - acc: 0.9370 - ETA: 3s - loss: 0.1934 - acc: 0.9381 - ETA: 3s - loss: 0.1942 - acc: 0.9377 - ETA: 3s - loss: 0.1955 - acc: 0.9374 - ETA: 3s - loss: 0.1956 - acc: 0.9370 - ETA: 3s - loss: 0.1951 - acc: 0.9372 - ETA: 3s - loss: 0.1943 - acc: 0.9374 - ETA: 3s - loss: 0.1927 - acc: 0.9376 - ETA: 3s - loss: 0.1943 - acc: 0.9376 - ETA: 2s - loss: 0.1982 - acc: 0.9370 - ETA: 2s - loss: 0.1975 - acc: 0.9372 - ETA: 2s - loss: 0.1978 - acc: 0.9369 - ETA: 2s - loss: 0.1989 - acc: 0.9366 - ETA: 2s - loss: 0.2019 - acc: 0.9361 - ETA: 2s - loss: 0.2081 - acc: 0.9348 - ETA: 2s - loss: 0.2066 - acc: 0.9353 - ETA: 2s - loss: 0.2064 - acc: 0.9350 - ETA: 2s - loss: 0.2062 - acc: 0.9352 - ETA: 2s - loss: 0.2044 - acc: 0.9359 - ETA: 2s - loss: 0.2054 - acc: 0.9356 - ETA: 2s - loss: 0.2060 - acc: 0.9349 - ETA: 2s - loss: 0.2051 - acc: 0.9351 - ETA: 2s - loss: 0.2071 - acc: 0.9349 - ETA: 2s - loss: 0.2070 - acc: 0.9349 - ETA: 1s - loss: 0.2100 - acc: 0.9346 - ETA: 1s - loss: 0.2096 - acc: 0.9348 - ETA: 1s - loss: 0.2082 - acc: 0.9350 - ETA: 1s - loss: 0.2069 - acc: 0.9356 - ETA: 1s - loss: 0.2080 - acc: 0.9354 - ETA: 1s - loss: 0.2077 - acc: 0.9354 - ETA: 1s - loss: 0.2085 - acc: 0.9359 - ETA: 1s - loss: 0.2132 - acc: 0.9349 - ETA: 1s - loss: 0.2134 - acc: 0.9349 - ETA: 1s - loss: 0.2132 - acc: 0.9351 - ETA: 1s - loss: 0.2121 - acc: 0.9352 - ETA: 1s - loss: 0.2162 - acc: 0.9347 - ETA: 1s - loss: 0.2171 - acc: 0.9345 - ETA: 1s - loss: 0.2177 - acc: 0.9345 - ETA: 1s - loss: 0.2177 - acc: 0.9341 - ETA: 1s - loss: 0.2192 - acc: 0.9336 - ETA: 0s - loss: 0.2191 - acc: 0.9337 - ETA: 0s - loss: 0.2197 - acc: 0.9334 - ETA: 0s - loss: 0.2188 - acc: 0.9334 - ETA: 0s - loss: 0.2183 - acc: 0.9334 - ETA: 0s - loss: 0.2182 - acc: 0.9337 - ETA: 0s - loss: 0.2178 - acc: 0.9339 - ETA: 0s - loss: 0.2181 - acc: 0.9337 - ETA: 0s - loss: 0.2210 - acc: 0.9327 - ETA: 0s - loss: 0.2207 - acc: 0.9331 - ETA: 0s - loss: 0.2203 - acc: 0.9329 - ETA: 0s - loss: 0.2205 - acc: 0.9328 - ETA: 0s - loss: 0.2201 - acc: 0.9326 - ETA: 0s - loss: 0.2224 - acc: 0.9317 - ETA: 0s - loss: 0.2236 - acc: 0.9314 - ETA: 0s - loss: 0.2232 - acc: 0.9316Epoch 00005: val_loss did not improve 6680/6680 [==============================] - 7s - loss: 0.2238 - acc: 0.9314 - val_loss: 0.5198 - val_acc: 0.8647 Epoch 7/20 6620/6680 [============================>.] - ETA: 7s - loss: 0.0120 - acc: 1.0000 - ETA: 6s - loss: 0.0314 - acc: 0.9875 - ETA: 6s - loss: 0.0841 - acc: 0.9714 - ETA: 6s - loss: 0.1783 - acc: 0.9550 - ETA: 6s - loss: 0.1598 - acc: 0.9615 - ETA: 6s - loss: 0.1698 - acc: 0.9562 - ETA: 6s - loss: 0.1943 - acc: 0.9447 - ETA: 6s - loss: 0.1962 - acc: 0.9341 - ETA: 6s - loss: 0.1907 - acc: 0.9340 - ETA: 6s - loss: 0.1915 - acc: 0.9357 - ETA: 6s - loss: 0.1866 - acc: 0.9355 - ETA: 6s - loss: 0.1879 - acc: 0.9324 - ETA: 6s - loss: 0.1906 - acc: 0.9311 - ETA: 6s - loss: 0.2003 - acc: 0.9300 - ETA: 6s - loss: 0.2009 - acc: 0.9291 - ETA: 6s - loss: 0.1899 - acc: 0.9337 - ETA: 6s - loss: 0.1847 - acc: 0.9357 - ETA: 6s - loss: 0.1816 - acc: 0.9375 - ETA: 5s - loss: 0.1840 - acc: 0.9355 - ETA: 5s - loss: 0.1798 - acc: 0.9362 - ETA: 5s - loss: 0.1827 - acc: 0.9336 - ETA: 5s - loss: 0.1840 - acc: 0.9320 - ETA: 5s - loss: 0.1867 - acc: 0.9321 - ETA: 5s - loss: 0.1842 - acc: 0.9336 - ETA: 5s - loss: 0.1915 - acc: 0.9336 - ETA: 5s - loss: 0.1889 - acc: 0.9342 - ETA: 5s - loss: 0.1874 - acc: 0.9348 - ETA: 5s - loss: 0.1860 - acc: 0.9360 - ETA: 5s - loss: 0.1806 - acc: 0.9382 - ETA: 5s - loss: 0.1816 - acc: 0.9381 - ETA: 5s - loss: 0.1846 - acc: 0.9374 - ETA: 5s - loss: 0.1895 - acc: 0.9362 - ETA: 5s - loss: 0.1893 - acc: 0.9366 - ETA: 4s - loss: 0.1889 - acc: 0.9370 - ETA: 4s - loss: 0.1865 - acc: 0.9374 - ETA: 4s - loss: 0.1893 - acc: 0.9373 - ETA: 4s - loss: 0.1848 - acc: 0.9390 - ETA: 4s - loss: 0.1824 - acc: 0.9393 - ETA: 4s - loss: 0.1851 - acc: 0.9383 - ETA: 4s - loss: 0.1884 - acc: 0.9373 - ETA: 4s - loss: 0.1953 - acc: 0.9364 - ETA: 4s - loss: 0.1932 - acc: 0.9367 - ETA: 4s - loss: 0.1901 - acc: 0.9378 - ETA: 4s - loss: 0.1895 - acc: 0.9381 - ETA: 4s - loss: 0.1881 - acc: 0.9380 - ETA: 4s - loss: 0.1870 - acc: 0.9375 - ETA: 4s - loss: 0.1894 - acc: 0.9367 - ETA: 4s - loss: 0.1893 - acc: 0.9359 - ETA: 4s - loss: 0.1910 - acc: 0.9362 - ETA: 3s - loss: 0.1898 - acc: 0.9365 - ETA: 3s - loss: 0.1914 - acc: 0.9358 - ETA: 3s - loss: 0.1898 - acc: 0.9360 - ETA: 3s - loss: 0.1877 - acc: 0.9366 - ETA: 3s - loss: 0.1899 - acc: 0.9359 - ETA: 3s - loss: 0.1898 - acc: 0.9359 - ETA: 3s - loss: 0.1886 - acc: 0.9367 - ETA: 3s - loss: 0.1878 - acc: 0.9373 - ETA: 3s - loss: 0.1859 - acc: 0.9381 - ETA: 3s - loss: 0.1858 - acc: 0.9377 - ETA: 3s - loss: 0.1864 - acc: 0.9376 - ETA: 3s - loss: 0.1866 - acc: 0.9381 - ETA: 3s - loss: 0.1854 - acc: 0.9386 - ETA: 3s - loss: 0.1866 - acc: 0.9385 - ETA: 3s - loss: 0.1892 - acc: 0.9371 - ETA: 3s - loss: 0.1892 - acc: 0.9368 - ETA: 2s - loss: 0.1903 - acc: 0.9365 - ETA: 2s - loss: 0.1902 - acc: 0.9362 - ETA: 2s - loss: 0.1911 - acc: 0.9361 - ETA: 2s - loss: 0.1930 - acc: 0.9366 - ETA: 2s - loss: 0.1914 - acc: 0.9370 - ETA: 2s - loss: 0.1926 - acc: 0.9370 - ETA: 2s - loss: 0.1941 - acc: 0.9369 - ETA: 2s - loss: 0.1946 - acc: 0.9366 - ETA: 2s - loss: 0.1964 - acc: 0.9364 - ETA: 2s - loss: 0.1976 - acc: 0.9361 - ETA: 2s - loss: 0.1995 - acc: 0.9356 - ETA: 2s - loss: 0.2007 - acc: 0.9356 - ETA: 2s - loss: 0.2004 - acc: 0.9353 - ETA: 2s - loss: 0.2005 - acc: 0.9355 - ETA: 2s - loss: 0.1998 - acc: 0.9357 - ETA: 1s - loss: 0.1991 - acc: 0.9357 - ETA: 1s - loss: 0.1976 - acc: 0.9361 - ETA: 1s - loss: 0.1964 - acc: 0.9362 - ETA: 1s - loss: 0.1975 - acc: 0.9360 - ETA: 1s - loss: 0.1970 - acc: 0.9364 - ETA: 1s - loss: 0.1958 - acc: 0.9365 - ETA: 1s - loss: 0.1970 - acc: 0.9367 - ETA: 1s - loss: 0.1979 - acc: 0.9366 - ETA: 1s - loss: 0.1979 - acc: 0.9368 - ETA: 1s - loss: 0.1972 - acc: 0.9371 - ETA: 1s - loss: 0.1978 - acc: 0.9371 - ETA: 1s - loss: 0.1983 - acc: 0.9365 - ETA: 1s - loss: 0.1994 - acc: 0.9363 - ETA: 1s - loss: 0.1994 - acc: 0.9364 - ETA: 1s - loss: 0.1998 - acc: 0.9364 - ETA: 1s - loss: 0.1994 - acc: 0.9364 - ETA: 0s - loss: 0.2005 - acc: 0.9356 - ETA: 0s - loss: 0.1993 - acc: 0.9358 - ETA: 0s - loss: 0.1989 - acc: 0.9359 - ETA: 0s - loss: 0.1998 - acc: 0.9361 - ETA: 0s - loss: 0.1993 - acc: 0.9359 - ETA: 0s - loss: 0.1990 - acc: 0.9360 - ETA: 0s - loss: 0.1995 - acc: 0.9357 - ETA: 0s - loss: 0.1985 - acc: 0.9360 - ETA: 0s - loss: 0.1988 - acc: 0.9358 - ETA: 0s - loss: 0.1974 - acc: 0.9362 - ETA: 0s - loss: 0.1982 - acc: 0.9362 - ETA: 0s - loss: 0.1984 - acc: 0.9365 - ETA: 0s - loss: 0.1986 - acc: 0.9363 - ETA: 0s - loss: 0.1980 - acc: 0.9366 - ETA: 0s - loss: 0.1985 - acc: 0.9364Epoch 00006: val_loss did not improve 6680/6680 [==============================] - 7s - loss: 0.1981 - acc: 0.9364 - val_loss: 0.5117 - val_acc: 0.8539 Epoch 8/20 6660/6680 [============================>.] - ETA: 6s - loss: 0.0584 - acc: 1.0000 - ETA: 6s - loss: 0.1994 - acc: 0.9500 - ETA: 6s - loss: 0.1597 - acc: 0.9571 - ETA: 6s - loss: 0.1994 - acc: 0.9500 - ETA: 6s - loss: 0.2007 - acc: 0.9538 - ETA: 6s - 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acc: 0.9541 - ETA: 3s - loss: 0.1615 - acc: 0.9529 - ETA: 3s - loss: 0.1609 - acc: 0.9526 - ETA: 3s - loss: 0.1604 - acc: 0.9528 - ETA: 3s - loss: 0.1621 - acc: 0.9519 - ETA: 3s - loss: 0.1625 - acc: 0.9516 - ETA: 3s - loss: 0.1639 - acc: 0.9513 - ETA: 3s - loss: 0.1646 - acc: 0.9513 - ETA: 2s - loss: 0.1630 - acc: 0.9518 - ETA: 2s - loss: 0.1656 - acc: 0.9500 - ETA: 2s - loss: 0.1642 - acc: 0.9505 - ETA: 2s - loss: 0.1622 - acc: 0.9512 - ETA: 2s - loss: 0.1667 - acc: 0.9502 - ETA: 2s - loss: 0.1681 - acc: 0.9495 - ETA: 2s - loss: 0.1679 - acc: 0.9498 - ETA: 2s - loss: 0.1675 - acc: 0.9498 - ETA: 2s - loss: 0.1674 - acc: 0.9495 - ETA: 2s - loss: 0.1706 - acc: 0.9493 - ETA: 2s - loss: 0.1716 - acc: 0.9484 - ETA: 2s - loss: 0.1701 - acc: 0.9489 - ETA: 2s - loss: 0.1693 - acc: 0.9491 - ETA: 2s - loss: 0.1707 - acc: 0.9485 - ETA: 2s - loss: 0.1708 - acc: 0.9485 - ETA: 2s - loss: 0.1692 - acc: 0.9489 - ETA: 1s - loss: 0.1695 - acc: 0.9485 - ETA: 1s - loss: 0.1688 - acc: 0.9488 - ETA: 1s - loss: 0.1685 - acc: 0.9490 - ETA: 1s - loss: 0.1699 - acc: 0.9488 - ETA: 1s - loss: 0.1692 - acc: 0.9490 - ETA: 1s - loss: 0.1691 - acc: 0.9490 - ETA: 1s - loss: 0.1698 - acc: 0.9486 - ETA: 1s - loss: 0.1700 - acc: 0.9485 - ETA: 1s - loss: 0.1711 - acc: 0.9483 - ETA: 1s - loss: 0.1707 - acc: 0.9487 - ETA: 1s - loss: 0.1729 - acc: 0.9483 - ETA: 1s - loss: 0.1741 - acc: 0.9485 - ETA: 1s - loss: 0.1733 - acc: 0.9487 - ETA: 1s - loss: 0.1728 - acc: 0.9487 - ETA: 1s - loss: 0.1725 - acc: 0.9488 - ETA: 1s - loss: 0.1730 - acc: 0.9484 - ETA: 0s - loss: 0.1730 - acc: 0.9479 - ETA: 0s - loss: 0.1752 - acc: 0.9476 - ETA: 0s - loss: 0.1753 - acc: 0.9471 - ETA: 0s - loss: 0.1748 - acc: 0.9471 - ETA: 0s - loss: 0.1745 - acc: 0.9473 - ETA: 0s - loss: 0.1731 - acc: 0.9477 - ETA: 0s - loss: 0.1727 - acc: 0.9479 - ETA: 0s - loss: 0.1719 - acc: 0.9481 - ETA: 0s - loss: 0.1723 - acc: 0.9478 - ETA: 0s - loss: 0.1723 - acc: 0.9475 - ETA: 0s - loss: 0.1717 - acc: 0.9475 - ETA: 0s - loss: 0.1708 - acc: 0.9477 - ETA: 0s - loss: 0.1740 - acc: 0.9469 - ETA: 0s - loss: 0.1750 - acc: 0.9469 - ETA: 0s - loss: 0.1764 - acc: 0.9464 - ETA: 0s - loss: 0.1774 - acc: 0.9458Epoch 00007: val_loss did not improve 6680/6680 [==============================] - 7s - loss: 0.1770 - acc: 0.9460 - val_loss: 0.5708 - val_acc: 0.8515 Epoch 9/20 6620/6680 [============================>.] - ETA: 6s - loss: 0.4235 - acc: 0.8500 - ETA: 6s - loss: 0.1940 - acc: 0.9375 - ETA: 6s - loss: 0.1980 - acc: 0.9071 - ETA: 6s - loss: 0.2069 - acc: 0.9100 - ETA: 6s - loss: 0.1829 - acc: 0.9231 - ETA: 6s - loss: 0.1802 - acc: 0.9281 - ETA: 6s - loss: 0.1602 - acc: 0.9395 - ETA: 6s - loss: 0.1432 - acc: 0.9477 - ETA: 6s - loss: 0.1530 - acc: 0.9460 - ETA: 6s - loss: 0.1527 - acc: 0.9464 - ETA: 6s - loss: 0.1464 - acc: 0.9484 - ETA: 6s - loss: 0.1479 - acc: 0.9471 - ETA: 6s - loss: 0.1546 - acc: 0.9486 - ETA: 6s - loss: 0.1480 - acc: 0.9513 - ETA: 6s - loss: 0.1531 - acc: 0.9476 - ETA: 6s - loss: 0.1602 - acc: 0.9467 - ETA: 6s - loss: 0.1599 - acc: 0.9479 - ETA: 6s - loss: 0.1702 - acc: 0.9461 - ETA: 6s - loss: 0.1693 - acc: 0.9472 - ETA: 5s - loss: 0.1698 - acc: 0.9474 - ETA: 5s - loss: 0.1683 - acc: 0.9467 - ETA: 5s - loss: 0.1621 - acc: 0.9492 - ETA: 5s - loss: 0.1583 - acc: 0.9508 - ETA: 5s - loss: 0.1668 - acc: 0.9493 - ETA: 5s - loss: 0.1616 - acc: 0.9507 - ETA: 5s - loss: 0.1578 - acc: 0.9513 - ETA: 5s - loss: 0.1542 - acc: 0.9519 - ETA: 5s - loss: 0.1573 - acc: 0.9506 - ETA: 5s - loss: 0.1578 - acc: 0.9506 - ETA: 5s - loss: 0.1562 - acc: 0.9517 - ETA: 5s - loss: 0.1651 - acc: 0.9506 - ETA: 5s - loss: 0.1609 - acc: 0.9516 - ETA: 5s - loss: 0.1566 - acc: 0.9531 - ETA: 5s - loss: 0.1565 - acc: 0.9530 - ETA: 4s - loss: 0.1555 - acc: 0.9529 - ETA: 4s - loss: 0.1544 - acc: 0.9533 - ETA: 4s - loss: 0.1568 - acc: 0.9532 - ETA: 4s - loss: 0.1540 - acc: 0.9536 - ETA: 4s - loss: 0.1510 - acc: 0.9544 - ETA: 4s - loss: 0.1505 - acc: 0.9547 - ETA: 4s - loss: 0.1500 - acc: 0.9550 - ETA: 4s - loss: 0.1476 - acc: 0.9557 - ETA: 4s - loss: 0.1468 - acc: 0.9560 - 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acc: 0.9507 - ETA: 2s - loss: 0.1675 - acc: 0.9510 - ETA: 2s - loss: 0.1668 - acc: 0.9507 - ETA: 2s - loss: 0.1690 - acc: 0.9498 - ETA: 2s - loss: 0.1672 - acc: 0.9502 - ETA: 2s - loss: 0.1676 - acc: 0.9498 - ETA: 2s - loss: 0.1702 - acc: 0.9498 - ETA: 2s - loss: 0.1697 - acc: 0.9500 - ETA: 2s - loss: 0.1684 - acc: 0.9504 - ETA: 2s - loss: 0.1702 - acc: 0.9502 - ETA: 2s - loss: 0.1732 - acc: 0.9496 - ETA: 2s - loss: 0.1715 - acc: 0.9498 - ETA: 2s - loss: 0.1714 - acc: 0.9498 - ETA: 1s - loss: 0.1696 - acc: 0.9504 - ETA: 1s - loss: 0.1682 - acc: 0.9508 - ETA: 1s - loss: 0.1668 - acc: 0.9510 - ETA: 1s - loss: 0.1665 - acc: 0.9510 - ETA: 1s - loss: 0.1654 - acc: 0.9512 - ETA: 1s - loss: 0.1644 - acc: 0.9514 - ETA: 1s - loss: 0.1666 - acc: 0.9506 - ETA: 1s - loss: 0.1671 - acc: 0.9502 - ETA: 1s - loss: 0.1666 - acc: 0.9502 - ETA: 1s - loss: 0.1662 - acc: 0.9502 - ETA: 1s - loss: 0.1660 - acc: 0.9504 - ETA: 1s - loss: 0.1652 - acc: 0.9505 - ETA: 1s - loss: 0.1651 - acc: 0.9507 - ETA: 1s - loss: 0.1650 - acc: 0.9507 - ETA: 1s - loss: 0.1637 - acc: 0.9512 - ETA: 1s - loss: 0.1631 - acc: 0.9510 - ETA: 0s - loss: 0.1631 - acc: 0.9510 - ETA: 0s - loss: 0.1634 - acc: 0.9509 - ETA: 0s - loss: 0.1647 - acc: 0.9502 - ETA: 0s - loss: 0.1650 - acc: 0.9498 - ETA: 0s - loss: 0.1657 - acc: 0.9492 - ETA: 0s - loss: 0.1649 - acc: 0.9495 - ETA: 0s - loss: 0.1647 - acc: 0.9493 - ETA: 0s - loss: 0.1643 - acc: 0.9490 - ETA: 0s - loss: 0.1689 - acc: 0.9481 - ETA: 0s - loss: 0.1689 - acc: 0.9481 - ETA: 0s - loss: 0.1677 - acc: 0.9484 - ETA: 0s - loss: 0.1678 - acc: 0.9484 - ETA: 0s - loss: 0.1673 - acc: 0.9485 - ETA: 0s - loss: 0.1660 - acc: 0.9489 - ETA: 0s - loss: 0.1649 - acc: 0.9494Epoch 00008: val_loss did not improve 6680/6680 [==============================] - 7s - loss: 0.1667 - acc: 0.9488 - val_loss: 0.5592 - val_acc: 0.8707 Epoch 10/20 6620/6680 [============================>.] - ETA: 6s - loss: 0.1232 - acc: 0.9500 - ETA: 6s - loss: 0.1668 - acc: 0.9375 - ETA: 6s - loss: 0.1685 - acc: 0.9429 - 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acc: 0.9571 - ETA: 5s - loss: 0.1678 - acc: 0.9569 - ETA: 5s - loss: 0.1682 - acc: 0.9567 - ETA: 5s - loss: 0.1666 - acc: 0.9565 - ETA: 5s - loss: 0.1657 - acc: 0.9568 - ETA: 5s - loss: 0.1664 - acc: 0.9556 - ETA: 4s - loss: 0.1724 - acc: 0.9534 - ETA: 4s - loss: 0.1699 - acc: 0.9543 - ETA: 4s - loss: 0.1684 - acc: 0.9542 - ETA: 4s - loss: 0.1665 - acc: 0.9545 - ETA: 4s - loss: 0.1701 - acc: 0.9535 - ETA: 4s - loss: 0.1667 - acc: 0.9547 - ETA: 4s - loss: 0.1660 - acc: 0.9546 - ETA: 4s - loss: 0.1675 - acc: 0.9545 - ETA: 4s - loss: 0.1647 - acc: 0.9556 - ETA: 4s - loss: 0.1634 - acc: 0.9554 - ETA: 4s - loss: 0.1610 - acc: 0.9561 - ETA: 4s - loss: 0.1598 - acc: 0.9567 - ETA: 4s - loss: 0.1596 - acc: 0.9569 - ETA: 4s - loss: 0.1597 - acc: 0.9567 - ETA: 4s - loss: 0.1598 - acc: 0.9566 - ETA: 4s - loss: 0.1600 - acc: 0.9561 - ETA: 3s - loss: 0.1594 - acc: 0.9564 - ETA: 3s - loss: 0.1600 - acc: 0.9556 - ETA: 3s - loss: 0.1583 - acc: 0.9561 - ETA: 3s - loss: 0.1601 - acc: 0.9554 - ETA: 3s - loss: 0.1584 - acc: 0.9556 - ETA: 3s - loss: 0.1596 - acc: 0.9552 - ETA: 3s - loss: 0.1593 - acc: 0.9551 - ETA: 3s - loss: 0.1596 - acc: 0.9547 - ETA: 3s - loss: 0.1573 - acc: 0.9552 - ETA: 3s - loss: 0.1567 - acc: 0.9551 - ETA: 3s - loss: 0.1565 - acc: 0.9550 - ETA: 3s - loss: 0.1574 - acc: 0.9547 - ETA: 3s - loss: 0.1555 - acc: 0.9551 - ETA: 3s - loss: 0.1541 - acc: 0.9556 - ETA: 3s - loss: 0.1535 - acc: 0.9550 - ETA: 2s - loss: 0.1526 - acc: 0.9552 - ETA: 2s - loss: 0.1523 - acc: 0.9554 - ETA: 2s - loss: 0.1530 - acc: 0.9555 - ETA: 2s - loss: 0.1540 - acc: 0.9554 - ETA: 2s - loss: 0.1548 - acc: 0.9554 - ETA: 2s - loss: 0.1545 - acc: 0.9555 - ETA: 2s - loss: 0.1559 - acc: 0.9545 - ETA: 2s - loss: 0.1551 - acc: 0.9549 - ETA: 2s - loss: 0.1564 - acc: 0.9541 - ETA: 2s - loss: 0.1572 - acc: 0.9541 - ETA: 2s - loss: 0.1568 - acc: 0.9543 - ETA: 2s - loss: 0.1580 - acc: 0.9542 - ETA: 2s - loss: 0.1584 - acc: 0.9539 - ETA: 2s - loss: 0.1579 - acc: 0.9539 - ETA: 2s - loss: 0.1569 - acc: 0.9540 - ETA: 2s - loss: 0.1583 - acc: 0.9538 - ETA: 1s - loss: 0.1578 - acc: 0.9537 - ETA: 1s - loss: 0.1578 - acc: 0.9533 - ETA: 1s - loss: 0.1574 - acc: 0.9536 - ETA: 1s - loss: 0.1558 - acc: 0.9542 - ETA: 1s - loss: 0.1548 - acc: 0.9545 - ETA: 1s - loss: 0.1546 - acc: 0.9545 - ETA: 1s - loss: 0.1537 - acc: 0.9548 - ETA: 1s - loss: 0.1526 - acc: 0.9552 - ETA: 1s - loss: 0.1514 - acc: 0.9557 - ETA: 1s - loss: 0.1502 - acc: 0.9558 - ETA: 1s - loss: 0.1499 - acc: 0.9554 - ETA: 1s - loss: 0.1531 - acc: 0.9547 - ETA: 1s - loss: 0.1522 - acc: 0.9551 - ETA: 1s - loss: 0.1523 - acc: 0.9548 - ETA: 1s - loss: 0.1525 - acc: 0.9546 - ETA: 1s - loss: 0.1515 - acc: 0.9549 - ETA: 0s - loss: 0.1519 - acc: 0.9543 - ETA: 0s - loss: 0.1517 - acc: 0.9543 - ETA: 0s - loss: 0.1507 - acc: 0.9547 - ETA: 0s - loss: 0.1507 - acc: 0.9549 - ETA: 0s - loss: 0.1498 - acc: 0.9550 - ETA: 0s - loss: 0.1485 - acc: 0.9554 - ETA: 0s - loss: 0.1484 - acc: 0.9555 - ETA: 0s - loss: 0.1475 - acc: 0.9556 - ETA: 0s - loss: 0.1473 - acc: 0.9559 - ETA: 0s - loss: 0.1477 - acc: 0.9559 - ETA: 0s - loss: 0.1472 - acc: 0.9558 - ETA: 0s - loss: 0.1498 - acc: 0.9557 - ETA: 0s - loss: 0.1494 - acc: 0.9557 - ETA: 0s - loss: 0.1494 - acc: 0.9556 - ETA: 0s - loss: 0.1484 - acc: 0.9559Epoch 00009: val_loss did not improve 6680/6680 [==============================] - 7s - loss: 0.1501 - acc: 0.9558 - val_loss: 0.5725 - val_acc: 0.8479 Epoch 11/20 6660/6680 [============================>.] - ETA: 7s - loss: 0.0180 - acc: 1.0000 - ETA: 6s - loss: 0.0517 - acc: 0.9750 - ETA: 6s - loss: 0.0865 - acc: 0.9714 - ETA: 6s - loss: 0.0666 - acc: 0.9750 - ETA: 6s - loss: 0.0605 - acc: 0.9769 - ETA: 6s - loss: 0.1065 - acc: 0.9687 - ETA: 6s - loss: 0.1143 - acc: 0.9684 - ETA: 6s - loss: 0.1107 - acc: 0.9682 - ETA: 6s - loss: 0.1245 - acc: 0.9640 - ETA: 6s - loss: 0.1167 - acc: 0.9643 - ETA: 6s - loss: 0.1147 - acc: 0.9661 - ETA: 6s - loss: 0.1059 - acc: 0.9691 - ETA: 6s - loss: 0.1012 - acc: 0.9703 - ETA: 6s - loss: 0.0949 - acc: 0.9725 - ETA: 6s - loss: 0.0951 - acc: 0.9709 - ETA: 6s - loss: 0.0910 - acc: 0.9717 - ETA: 6s - loss: 0.0940 - acc: 0.9714 - ETA: 6s - loss: 0.0906 - acc: 0.9731 - ETA: 5s - loss: 0.0936 - acc: 0.9736 - ETA: 5s - loss: 0.0990 - acc: 0.9724 - ETA: 5s - loss: 0.0975 - acc: 0.9721 - ETA: 5s - loss: 0.0999 - acc: 0.9703 - ETA: 5s - loss: 0.0968 - acc: 0.9716 - ETA: 5s - loss: 0.0943 - acc: 0.9721 - ETA: 5s - loss: 0.0938 - acc: 0.9719 - ETA: 5s - loss: 0.0947 - acc: 0.9717 - ETA: 5s - loss: 0.0952 - acc: 0.9715 - ETA: 5s - loss: 0.0940 - acc: 0.9720 - ETA: 5s - loss: 0.0938 - acc: 0.9718 - ETA: 5s - loss: 0.0976 - acc: 0.9705 - ETA: 5s - loss: 0.0960 - acc: 0.9709 - ETA: 5s - loss: 0.0951 - acc: 0.9702 - ETA: 4s - loss: 0.0958 - acc: 0.9706 - ETA: 4s - loss: 0.1045 - acc: 0.9690 - ETA: 4s - loss: 0.1071 - acc: 0.9680 - ETA: 4s - loss: 0.1052 - acc: 0.9684 - ETA: 4s - loss: 0.1049 - acc: 0.9683 - ETA: 4s - loss: 0.1060 - acc: 0.9683 - ETA: 4s - loss: 0.1076 - acc: 0.9683 - ETA: 4s - loss: 0.1103 - acc: 0.9674 - ETA: 4s - loss: 0.1124 - acc: 0.9661 - ETA: 4s - loss: 0.1129 - acc: 0.9657 - ETA: 4s - loss: 0.1114 - acc: 0.9661 - ETA: 4s - loss: 0.1119 - acc: 0.9662 - ETA: 4s - loss: 0.1109 - acc: 0.9662 - ETA: 4s - loss: 0.1095 - acc: 0.9665 - ETA: 4s - loss: 0.1091 - acc: 0.9665 - ETA: 4s - loss: 0.1089 - acc: 0.9662 - ETA: 3s - loss: 0.1150 - acc: 0.9655 - ETA: 3s - loss: 0.1153 - acc: 0.9652 - ETA: 3s - loss: 0.1201 - acc: 0.9642 - ETA: 3s - loss: 0.1236 - acc: 0.9640 - ETA: 3s - loss: 0.1248 - acc: 0.9634 - ETA: 3s - loss: 0.1254 - acc: 0.9634 - ETA: 3s - loss: 0.1256 - acc: 0.9632 - ETA: 3s - loss: 0.1245 - acc: 0.9636 - ETA: 3s - loss: 0.1258 - acc: 0.9627 - ETA: 3s - loss: 0.1261 - acc: 0.9625 - ETA: 3s - loss: 0.1256 - acc: 0.9629 - ETA: 3s - loss: 0.1260 - acc: 0.9626 - ETA: 3s - loss: 0.1271 - acc: 0.9619 - ETA: 3s - loss: 0.1266 - acc: 0.9620 - ETA: 3s - loss: 0.1287 - acc: 0.9615 - ETA: 3s - loss: 0.1298 - acc: 0.9613 - ETA: 2s - loss: 0.1315 - acc: 0.9606 - ETA: 2s - loss: 0.1381 - acc: 0.9597 - ETA: 2s - loss: 0.1402 - acc: 0.9590 - ETA: 2s - loss: 0.1387 - acc: 0.9594 - ETA: 2s - loss: 0.1383 - acc: 0.9595 - ETA: 2s - loss: 0.1400 - acc: 0.9596 - ETA: 2s - loss: 0.1435 - acc: 0.9595 - ETA: 2s - loss: 0.1425 - acc: 0.9596 - ETA: 2s - loss: 0.1411 - acc: 0.9599 - ETA: 2s - loss: 0.1411 - acc: 0.9598 - ETA: 2s - loss: 0.1403 - acc: 0.9599 - ETA: 2s - loss: 0.1404 - acc: 0.9595 - ETA: 2s - loss: 0.1401 - acc: 0.9594 - ETA: 2s - loss: 0.1389 - acc: 0.9597 - ETA: 2s - loss: 0.1389 - acc: 0.9598 - ETA: 2s - loss: 0.1388 - acc: 0.9597 - ETA: 1s - loss: 0.1384 - acc: 0.9598 - ETA: 1s - loss: 0.1412 - acc: 0.9586 - ETA: 1s - loss: 0.1399 - acc: 0.9589 - ETA: 1s - loss: 0.1417 - acc: 0.9588 - ETA: 1s - loss: 0.1412 - acc: 0.9591 - ETA: 1s - loss: 0.1413 - acc: 0.9590 - ETA: 1s - loss: 0.1411 - acc: 0.9591 - ETA: 1s - loss: 0.1412 - acc: 0.9590 - ETA: 1s - loss: 0.1404 - acc: 0.9593 - ETA: 1s - loss: 0.1406 - acc: 0.9590 - ETA: 1s - loss: 0.1397 - acc: 0.9593 - ETA: 1s - loss: 0.1393 - acc: 0.9590 - ETA: 1s - loss: 0.1396 - acc: 0.9591 - ETA: 1s - loss: 0.1388 - acc: 0.9591 - ETA: 1s - loss: 0.1385 - acc: 0.9592 - ETA: 1s - loss: 0.1384 - acc: 0.9591 - ETA: 0s - loss: 0.1384 - acc: 0.9594 - ETA: 0s - loss: 0.1379 - acc: 0.9595 - ETA: 0s - loss: 0.1378 - acc: 0.9594 - ETA: 0s - loss: 0.1392 - acc: 0.9589 - ETA: 0s - loss: 0.1410 - acc: 0.9588 - ETA: 0s - loss: 0.1409 - acc: 0.9589 - ETA: 0s - loss: 0.1407 - acc: 0.9588 - ETA: 0s - loss: 0.1407 - acc: 0.9587 - ETA: 0s - loss: 0.1400 - acc: 0.9590 - ETA: 0s - loss: 0.1395 - acc: 0.9590 - ETA: 0s - loss: 0.1387 - acc: 0.9593 - ETA: 0s - loss: 0.1384 - acc: 0.9593 - ETA: 0s - loss: 0.1376 - acc: 0.9596 - ETA: 0s - loss: 0.1386 - acc: 0.9593 - ETA: 0s - loss: 0.1393 - acc: 0.9595 - ETA: 0s - loss: 0.1393 - acc: 0.9593Epoch 00010: val_loss did not improve 6680/6680 [==============================] - 7s - loss: 0.1389 - acc: 0.9594 - val_loss: 0.5652 - val_acc: 0.8491 Epoch 12/20 6640/6680 [============================>.] - ETA: 6s - loss: 0.0324 - acc: 1.0000 - ETA: 6s - loss: 0.1854 - acc: 0.9750 - ETA: 6s - loss: 0.1316 - acc: 0.9714 - ETA: 6s - loss: 0.1121 - acc: 0.9700 - ETA: 6s - loss: 0.0965 - acc: 0.9731 - ETA: 6s - loss: 0.0825 - acc: 0.9781 - ETA: 6s - loss: 0.0777 - acc: 0.9789 - ETA: 6s - loss: 0.1003 - acc: 0.9773 - ETA: 6s - loss: 0.0970 - acc: 0.9740 - ETA: 6s - loss: 0.0952 - acc: 0.9750 - ETA: 6s - loss: 0.0967 - acc: 0.9742 - ETA: 6s - loss: 0.1112 - acc: 0.9662 - ETA: 5s - loss: 0.1134 - acc: 0.9635 - ETA: 5s - loss: 0.1100 - acc: 0.9650 - ETA: 5s - loss: 0.1108 - acc: 0.9663 - ETA: 5s - loss: 0.1111 - acc: 0.9663 - ETA: 5s - loss: 0.1155 - acc: 0.9653 - ETA: 5s - loss: 0.1118 - acc: 0.9663 - ETA: 5s - loss: 0.1111 - acc: 0.9664 - ETA: 5s - loss: 0.1060 - acc: 0.9681 - ETA: 5s - loss: 0.1145 - acc: 0.9656 - ETA: 5s - loss: 0.1131 - acc: 0.9656 - ETA: 5s - loss: 0.1120 - acc: 0.9642 - ETA: 5s - loss: 0.1116 - acc: 0.9636 - ETA: 5s - loss: 0.1177 - acc: 0.9630 - ETA: 5s - loss: 0.1169 - acc: 0.9632 - ETA: 5s - loss: 0.1140 - acc: 0.9639 - ETA: 5s - loss: 0.1120 - acc: 0.9646 - ETA: 5s - loss: 0.1163 - acc: 0.9635 - ETA: 4s - loss: 0.1222 - acc: 0.9631 - ETA: 4s - loss: 0.1217 - acc: 0.9626 - ETA: 4s - loss: 0.1226 - acc: 0.9622 - ETA: 4s - loss: 0.1197 - acc: 0.9634 - ETA: 4s - loss: 0.1170 - acc: 0.9640 - ETA: 4s - loss: 0.1182 - acc: 0.9636 - ETA: 4s - loss: 0.1163 - acc: 0.9642 - ETA: 4s - loss: 0.1142 - acc: 0.9647 - ETA: 4s - loss: 0.1192 - acc: 0.9643 - ETA: 4s - loss: 0.1183 - acc: 0.9648 - ETA: 4s - loss: 0.1194 - acc: 0.9644 - ETA: 4s - loss: 0.1183 - acc: 0.9649 - ETA: 4s - loss: 0.1194 - acc: 0.9645 - ETA: 4s - loss: 0.1191 - acc: 0.9646 - ETA: 4s - loss: 0.1196 - acc: 0.9642 - ETA: 4s - loss: 0.1208 - acc: 0.9632 - ETA: 4s - loss: 0.1209 - acc: 0.9632 - ETA: 4s - loss: 0.1236 - acc: 0.9622 - ETA: 3s - loss: 0.1249 - acc: 0.9616 - ETA: 3s - loss: 0.1264 - acc: 0.9610 - ETA: 3s - loss: 0.1254 - acc: 0.9615 - ETA: 3s - loss: 0.1241 - acc: 0.9616 - ETA: 3s - loss: 0.1265 - acc: 0.9604 - ETA: 3s - loss: 0.1254 - acc: 0.9605 - ETA: 3s - loss: 0.1243 - acc: 0.9609 - ETA: 3s - loss: 0.1242 - acc: 0.9607 - ETA: 3s - loss: 0.1248 - acc: 0.9602 - ETA: 3s - loss: 0.1270 - acc: 0.9598 - ETA: 3s - loss: 0.1308 - acc: 0.9587 - ETA: 3s - loss: 0.1300 - acc: 0.9586 - ETA: 3s - loss: 0.1300 - acc: 0.9584 - ETA: 3s - loss: 0.1299 - acc: 0.9583 - ETA: 3s - loss: 0.1286 - acc: 0.9587 - ETA: 3s - loss: 0.1272 - acc: 0.9591 - ETA: 3s - loss: 0.1261 - acc: 0.9595 - ETA: 2s - loss: 0.1263 - acc: 0.9591 - ETA: 2s - loss: 0.1274 - acc: 0.9590 - ETA: 2s - loss: 0.1260 - acc: 0.9593 - ETA: 2s - loss: 0.1255 - acc: 0.9595 - ETA: 2s - loss: 0.1239 - acc: 0.9600 - ETA: 2s - loss: 0.1284 - acc: 0.9587 - ETA: 2s - loss: 0.1324 - acc: 0.9583 - ETA: 2s - loss: 0.1327 - acc: 0.9585 - ETA: 2s - loss: 0.1328 - acc: 0.9586 - ETA: 2s - loss: 0.1327 - acc: 0.9587 - ETA: 2s - loss: 0.1367 - acc: 0.9577 - ETA: 2s - loss: 0.1359 - acc: 0.9580 - ETA: 2s - loss: 0.1352 - acc: 0.9581 - ETA: 2s - loss: 0.1348 - acc: 0.9584 - ETA: 2s - loss: 0.1359 - acc: 0.9583 - ETA: 2s - loss: 0.1347 - acc: 0.9586 - ETA: 1s - loss: 0.1334 - acc: 0.9592 - ETA: 1s - loss: 0.1339 - acc: 0.9593 - ETA: 1s - loss: 0.1333 - acc: 0.9596 - ETA: 1s - loss: 0.1343 - acc: 0.9590 - ETA: 1s - loss: 0.1340 - acc: 0.9593 - ETA: 1s - loss: 0.1338 - acc: 0.9592 - ETA: 1s - loss: 0.1325 - acc: 0.9597 - ETA: 1s - loss: 0.1312 - acc: 0.9602 - ETA: 1s - loss: 0.1301 - acc: 0.9604 - ETA: 1s - loss: 0.1299 - acc: 0.9605 - ETA: 1s - loss: 0.1289 - acc: 0.9607 - ETA: 1s - loss: 0.1297 - acc: 0.9604 - ETA: 1s - loss: 0.1305 - acc: 0.9603 - ETA: 1s - loss: 0.1311 - acc: 0.9602 - ETA: 1s - loss: 0.1332 - acc: 0.9598 - ETA: 1s - loss: 0.1332 - acc: 0.9597 - ETA: 0s - loss: 0.1324 - acc: 0.9598 - ETA: 0s - loss: 0.1313 - acc: 0.9602 - ETA: 0s - loss: 0.1305 - acc: 0.9602 - ETA: 0s - loss: 0.1303 - acc: 0.9605 - ETA: 0s - loss: 0.1297 - acc: 0.9605 - ETA: 0s - loss: 0.1288 - acc: 0.9606 - ETA: 0s - loss: 0.1283 - acc: 0.9607 - ETA: 0s - loss: 0.1290 - acc: 0.9606 - ETA: 0s - loss: 0.1284 - acc: 0.9606 - ETA: 0s - loss: 0.1290 - acc: 0.9604 - ETA: 0s - loss: 0.1288 - acc: 0.9606 - ETA: 0s - loss: 0.1295 - acc: 0.9605 - ETA: 0s - loss: 0.1305 - acc: 0.9602 - ETA: 0s - loss: 0.1304 - acc: 0.9603 - ETA: 0s - loss: 0.1299 - acc: 0.9605 - ETA: 0s - loss: 0.1293 - acc: 0.9607Epoch 00011: val_loss did not improve 6680/6680 [==============================] - 7s - loss: 0.1306 - acc: 0.9605 - val_loss: 0.5974 - val_acc: 0.8587 Epoch 13/20 6660/6680 [============================>.] - ETA: 6s - loss: 0.0423 - acc: 1.0000 - ETA: 6s - loss: 0.0686 - acc: 0.9875 - ETA: 6s - loss: 0.0861 - acc: 0.9786 - ETA: 6s - loss: 0.0701 - acc: 0.9800 - ETA: 6s - loss: 0.0660 - acc: 0.9769 - ETA: 6s - loss: 0.0736 - acc: 0.9781 - ETA: 6s - loss: 0.0655 - acc: 0.9816 - ETA: 6s - loss: 0.0687 - acc: 0.9818 - ETA: 6s - loss: 0.0663 - acc: 0.9800 - ETA: 6s - loss: 0.0656 - acc: 0.9786 - ETA: 6s - loss: 0.0694 - acc: 0.9774 - ETA: 6s - loss: 0.0656 - acc: 0.9794 - ETA: 6s - loss: 0.0640 - acc: 0.9797 - ETA: 6s - loss: 0.0824 - acc: 0.9762 - ETA: 6s - loss: 0.0809 - acc: 0.9767 - ETA: 5s - loss: 0.0919 - acc: 0.9717 - ETA: 5s - loss: 0.0938 - acc: 0.9704 - ETA: 5s - loss: 0.0968 - acc: 0.9702 - ETA: 5s - loss: 0.0991 - acc: 0.9700 - ETA: 5s - loss: 0.0970 - acc: 0.9707 - ETA: 5s - loss: 0.0969 - acc: 0.9705 - ETA: 5s - loss: 0.0937 - acc: 0.9711 - ETA: 5s - loss: 0.0921 - acc: 0.9716 - ETA: 5s - loss: 0.0919 - acc: 0.9721 - ETA: 5s - loss: 0.0929 - acc: 0.9712 - ETA: 5s - loss: 0.0980 - acc: 0.9704 - ETA: 5s - loss: 0.0961 - acc: 0.9709 - ETA: 5s - loss: 0.0950 - acc: 0.9713 - ETA: 5s - loss: 0.0961 - acc: 0.9706 - ETA: 5s - loss: 0.0995 - acc: 0.9693 - ETA: 5s - loss: 0.1005 - acc: 0.9681 - ETA: 5s - loss: 0.1032 - acc: 0.9676 - ETA: 4s - loss: 0.1057 - acc: 0.9660 - ETA: 4s - loss: 0.1070 - acc: 0.9655 - ETA: 4s - loss: 0.1073 - acc: 0.9650 - ETA: 4s - loss: 0.1047 - acc: 0.9660 - ETA: 4s - loss: 0.1072 - acc: 0.9656 - ETA: 4s - loss: 0.1061 - acc: 0.9661 - ETA: 4s - loss: 0.1059 - acc: 0.9661 - ETA: 4s - loss: 0.1105 - acc: 0.9657 - ETA: 4s - loss: 0.1133 - acc: 0.9649 - ETA: 4s - loss: 0.1120 - acc: 0.9653 - ETA: 4s - loss: 0.1151 - acc: 0.9646 - ETA: 4s - loss: 0.1154 - acc: 0.9650 - ETA: 4s - loss: 0.1144 - acc: 0.9650 - ETA: 4s - loss: 0.1130 - acc: 0.9651 - ETA: 4s - loss: 0.1131 - acc: 0.9644 - ETA: 4s - loss: 0.1120 - acc: 0.9648 - ETA: 4s - loss: 0.1111 - acc: 0.9648 - ETA: 3s - loss: 0.1096 - acc: 0.9652 - ETA: 3s - loss: 0.1098 - acc: 0.9646 - ETA: 3s - loss: 0.1101 - acc: 0.9643 - ETA: 3s - loss: 0.1097 - acc: 0.9646 - ETA: 3s - loss: 0.1094 - acc: 0.9648 - ETA: 3s - loss: 0.1083 - acc: 0.9648 - ETA: 3s - loss: 0.1079 - acc: 0.9642 - ETA: 3s - loss: 0.1079 - acc: 0.9643 - ETA: 3s - loss: 0.1062 - acc: 0.9649 - ETA: 3s - loss: 0.1089 - acc: 0.9641 - ETA: 3s - loss: 0.1084 - acc: 0.9641 - ETA: 3s - loss: 0.1100 - acc: 0.9644 - ETA: 3s - loss: 0.1095 - acc: 0.9648 - ETA: 3s - loss: 0.1079 - acc: 0.9653 - ETA: 3s - loss: 0.1085 - acc: 0.9648 - ETA: 3s - loss: 0.1083 - acc: 0.9651 - ETA: 2s - loss: 0.1068 - acc: 0.9656 - ETA: 2s - loss: 0.1060 - acc: 0.9659 - ETA: 2s - loss: 0.1064 - acc: 0.9659 - ETA: 2s - loss: 0.1053 - acc: 0.9664 - ETA: 2s - loss: 0.1060 - acc: 0.9664 - ETA: 2s - loss: 0.1069 - acc: 0.9657 - ETA: 2s - loss: 0.1068 - acc: 0.9657 - ETA: 2s - loss: 0.1067 - acc: 0.9657 - ETA: 2s - loss: 0.1066 - acc: 0.9658 - ETA: 2s - loss: 0.1059 - acc: 0.9660 - ETA: 2s - loss: 0.1065 - acc: 0.9658 - ETA: 2s - loss: 0.1061 - acc: 0.9660 - ETA: 2s - loss: 0.1072 - acc: 0.9654 - ETA: 2s - loss: 0.1113 - acc: 0.9652 - ETA: 2s - loss: 0.1107 - acc: 0.9652 - ETA: 2s - loss: 0.1099 - acc: 0.9654 - ETA: 1s - loss: 0.1104 - acc: 0.9654 - ETA: 1s - loss: 0.1115 - acc: 0.9652 - ETA: 1s - loss: 0.1112 - acc: 0.9655 - ETA: 1s - loss: 0.1104 - acc: 0.9657 - ETA: 1s - loss: 0.1103 - acc: 0.9655 - ETA: 1s - loss: 0.1110 - acc: 0.9651 - ETA: 1s - loss: 0.1102 - acc: 0.9653 - ETA: 1s - loss: 0.1098 - acc: 0.9653 - ETA: 1s - loss: 0.1090 - acc: 0.9655 - ETA: 1s - loss: 0.1107 - acc: 0.9652 - ETA: 1s - loss: 0.1122 - acc: 0.9648 - ETA: 1s - loss: 0.1121 - acc: 0.9649 - ETA: 1s - loss: 0.1112 - acc: 0.9652 - ETA: 1s - loss: 0.1106 - acc: 0.9651 - ETA: 1s - loss: 0.1098 - acc: 0.9654 - ETA: 0s - loss: 0.1114 - acc: 0.9651 - ETA: 0s - loss: 0.1109 - acc: 0.9651 - ETA: 0s - loss: 0.1123 - acc: 0.9650 - ETA: 0s - loss: 0.1123 - acc: 0.9650 - ETA: 0s - loss: 0.1117 - acc: 0.9652 - ETA: 0s - loss: 0.1112 - acc: 0.9652 - ETA: 0s - loss: 0.1119 - acc: 0.9649 - ETA: 0s - loss: 0.1113 - acc: 0.9650 - ETA: 0s - loss: 0.1139 - acc: 0.9643 - ETA: 0s - loss: 0.1152 - acc: 0.9641 - ETA: 0s - loss: 0.1150 - acc: 0.9638 - ETA: 0s - loss: 0.1154 - acc: 0.9637 - ETA: 0s - loss: 0.1154 - acc: 0.9639 - ETA: 0s - loss: 0.1154 - acc: 0.9636 - ETA: 0s - loss: 0.1146 - acc: 0.9639 - ETA: 0s - loss: 0.1170 - acc: 0.9637Epoch 00012: val_loss did not improve 6680/6680 [==============================] - 7s - loss: 0.1169 - acc: 0.9638 - val_loss: 0.6100 - val_acc: 0.8551 Epoch 14/20 6640/6680 [============================>.] - ETA: 6s - loss: 0.0154 - acc: 1.0000 - ETA: 6s - loss: 0.1874 - acc: 0.9500 - ETA: 6s - loss: 0.1601 - acc: 0.9571 - ETA: 6s - loss: 0.1212 - acc: 0.9650 - ETA: 6s - loss: 0.1260 - acc: 0.9692 - ETA: 6s - loss: 0.1382 - acc: 0.9719 - ETA: 6s - loss: 0.1313 - acc: 0.9711 - ETA: 6s - loss: 0.1292 - acc: 0.9705 - ETA: 6s - loss: 0.1158 - acc: 0.9740 - ETA: 6s - loss: 0.1193 - acc: 0.9696 - ETA: 6s - loss: 0.1136 - acc: 0.9694 - ETA: 6s - loss: 0.1074 - acc: 0.9691 - ETA: 6s - loss: 0.1118 - acc: 0.9676 - ETA: 5s - loss: 0.1095 - acc: 0.9662 - ETA: 5s - loss: 0.1160 - acc: 0.9640 - ETA: 5s - loss: 0.1140 - acc: 0.9641 - ETA: 5s - loss: 0.1141 - acc: 0.9653 - ETA: 5s - loss: 0.1085 - acc: 0.9673 - ETA: 5s - loss: 0.1056 - acc: 0.9682 - ETA: 5s - loss: 0.1129 - acc: 0.9664 - ETA: 5s - loss: 0.1120 - acc: 0.9656 - ETA: 5s - loss: 0.1119 - acc: 0.9648 - ETA: 5s - loss: 0.1091 - acc: 0.9649 - ETA: 5s - loss: 0.1081 - acc: 0.9657 - ETA: 5s - loss: 0.1076 - acc: 0.9658 - ETA: 5s - loss: 0.1061 - acc: 0.9664 - ETA: 5s - loss: 0.1043 - acc: 0.9665 - ETA: 5s - loss: 0.1026 - acc: 0.9665 - ETA: 5s - loss: 0.0996 - acc: 0.9676 - ETA: 4s - loss: 0.1005 - acc: 0.9676 - ETA: 4s - loss: 0.1020 - acc: 0.9670 - ETA: 4s - loss: 0.1035 - acc: 0.9665 - ETA: 4s - loss: 0.1011 - acc: 0.9670 - ETA: 4s - loss: 0.1022 - acc: 0.9665 - ETA: 4s - loss: 0.1010 - acc: 0.9670 - ETA: 4s - loss: 0.0989 - acc: 0.9679 - ETA: 4s - loss: 0.0972 - acc: 0.9683 - ETA: 4s - loss: 0.0958 - acc: 0.9687 - ETA: 4s - loss: 0.0974 - acc: 0.9687 - ETA: 4s - loss: 0.0953 - acc: 0.9695 - ETA: 4s - loss: 0.0940 - acc: 0.9698 - ETA: 4s - loss: 0.0922 - acc: 0.9706 - ETA: 4s - loss: 0.0920 - acc: 0.9709 - ETA: 4s - loss: 0.0937 - acc: 0.9704 - ETA: 4s - loss: 0.0953 - acc: 0.9703 - ETA: 4s - loss: 0.0985 - acc: 0.9695 - ETA: 4s - loss: 0.0979 - acc: 0.9698 - ETA: 3s - loss: 0.0984 - acc: 0.9701 - ETA: 3s - loss: 0.0986 - acc: 0.9700 - ETA: 3s - loss: 0.0982 - acc: 0.9699 - ETA: 3s - loss: 0.0977 - acc: 0.9702 - ETA: 3s - loss: 0.0981 - acc: 0.9705 - ETA: 3s - loss: 0.0976 - acc: 0.9701 - ETA: 3s - loss: 0.0966 - acc: 0.9703 - ETA: 3s - loss: 0.0991 - acc: 0.9696 - ETA: 3s - loss: 0.0992 - acc: 0.9693 - ETA: 3s - loss: 0.1013 - acc: 0.9683 - ETA: 3s - loss: 0.1005 - acc: 0.9683 - ETA: 3s - loss: 0.1010 - acc: 0.9680 - ETA: 3s - loss: 0.1044 - acc: 0.9674 - ETA: 3s - loss: 0.1050 - acc: 0.9674 - ETA: 3s - loss: 0.1054 - acc: 0.9674 - ETA: 3s - loss: 0.1072 - acc: 0.9674 - ETA: 3s - loss: 0.1085 - acc: 0.9666 - ETA: 2s - loss: 0.1089 - acc: 0.9658 - ETA: 2s - loss: 0.1123 - acc: 0.9651 - ETA: 2s - loss: 0.1148 - acc: 0.9651 - ETA: 2s - loss: 0.1162 - acc: 0.9653 - ETA: 2s - loss: 0.1147 - acc: 0.9659 - ETA: 2s - loss: 0.1134 - acc: 0.9663 - ETA: 2s - loss: 0.1139 - acc: 0.9664 - ETA: 2s - loss: 0.1131 - acc: 0.9666 - ETA: 2s - loss: 0.1117 - acc: 0.9671 - ETA: 2s - loss: 0.1119 - acc: 0.9670 - ETA: 2s - loss: 0.1109 - acc: 0.9673 - ETA: 2s - loss: 0.1111 - acc: 0.9673 - ETA: 2s - loss: 0.1105 - acc: 0.9675 - ETA: 2s - loss: 0.1095 - acc: 0.9677 - ETA: 2s - loss: 0.1090 - acc: 0.9679 - ETA: 2s - loss: 0.1086 - acc: 0.9681 - ETA: 1s - loss: 0.1093 - acc: 0.9681 - ETA: 1s - loss: 0.1091 - acc: 0.9681 - ETA: 1s - loss: 0.1084 - acc: 0.9681 - ETA: 1s - loss: 0.1085 - acc: 0.9681 - ETA: 1s - loss: 0.1084 - acc: 0.9679 - ETA: 1s - loss: 0.1076 - acc: 0.9680 - ETA: 1s - loss: 0.1069 - acc: 0.9682 - ETA: 1s - loss: 0.1070 - acc: 0.9684 - ETA: 1s - loss: 0.1059 - acc: 0.9687 - ETA: 1s - loss: 0.1065 - acc: 0.9680 - ETA: 1s - loss: 0.1077 - acc: 0.9676 - ETA: 1s - loss: 0.1095 - acc: 0.9674 - ETA: 1s - loss: 0.1090 - acc: 0.9674 - ETA: 1s - loss: 0.1086 - acc: 0.9674 - ETA: 1s - loss: 0.1086 - acc: 0.9672 - ETA: 1s - loss: 0.1087 - acc: 0.9672 - ETA: 0s - loss: 0.1081 - acc: 0.9672 - ETA: 0s - loss: 0.1100 - acc: 0.9672 - ETA: 0s - loss: 0.1104 - acc: 0.9668 - ETA: 0s - loss: 0.1097 - acc: 0.9670 - ETA: 0s - loss: 0.1093 - acc: 0.9672 - ETA: 0s - loss: 0.1096 - acc: 0.9672 - ETA: 0s - loss: 0.1105 - acc: 0.9670 - ETA: 0s - loss: 0.1096 - acc: 0.9673 - ETA: 0s - loss: 0.1089 - acc: 0.9673 - ETA: 0s - loss: 0.1085 - acc: 0.9671 - ETA: 0s - loss: 0.1086 - acc: 0.9670 - ETA: 0s - loss: 0.1077 - acc: 0.9673 - ETA: 0s - loss: 0.1069 - acc: 0.9676 - ETA: 0s - loss: 0.1084 - acc: 0.9670 - ETA: 0s - loss: 0.1078 - acc: 0.9670 - ETA: 0s - loss: 0.1082 - acc: 0.9669Epoch 00013: val_loss did not improve 6680/6680 [==============================] - 7s - loss: 0.1091 - acc: 0.9668 - val_loss: 0.6127 - val_acc: 0.8599 Epoch 15/20 6620/6680 [============================>.] - ETA: 6s - loss: 0.0152 - acc: 1.0000 - ETA: 6s - loss: 0.0877 - acc: 0.9750 - ETA: 6s - loss: 0.0689 - acc: 0.9786 - ETA: 6s - loss: 0.0548 - acc: 0.9850 - ETA: 6s - loss: 0.0625 - acc: 0.9846 - ETA: 6s - loss: 0.0563 - acc: 0.9844 - ETA: 6s - loss: 0.0512 - acc: 0.9868 - ETA: 6s - loss: 0.0488 - acc: 0.9864 - ETA: 6s - loss: 0.0442 - acc: 0.9880 - ETA: 6s - loss: 0.0495 - acc: 0.9857 - ETA: 6s - loss: 0.0514 - acc: 0.9855 - ETA: 6s - loss: 0.0571 - acc: 0.9824 - ETA: 6s - loss: 0.0607 - acc: 0.9824 - ETA: 5s - loss: 0.0582 - acc: 0.9837 - ETA: 5s - loss: 0.0716 - acc: 0.9814 - ETA: 5s - loss: 0.0728 - acc: 0.9804 - ETA: 5s - loss: 0.0749 - acc: 0.9806 - ETA: 5s - loss: 0.0741 - acc: 0.9808 - ETA: 5s - loss: 0.0707 - acc: 0.9818 - ETA: 5s - loss: 0.0696 - acc: 0.9819 - ETA: 5s - loss: 0.0720 - acc: 0.9820 - ETA: 5s - loss: 0.0739 - acc: 0.9812 - ETA: 5s - loss: 0.0728 - acc: 0.9806 - ETA: 5s - loss: 0.0718 - acc: 0.9807 - ETA: 5s - loss: 0.0718 - acc: 0.9808 - ETA: 5s - loss: 0.0726 - acc: 0.9803 - ETA: 5s - loss: 0.0744 - acc: 0.9791 - ETA: 5s - loss: 0.0856 - acc: 0.9762 - ETA: 5s - loss: 0.0835 - acc: 0.9771 - ETA: 4s - loss: 0.0827 - acc: 0.9773 - ETA: 4s - loss: 0.0814 - acc: 0.9775 - ETA: 4s - loss: 0.0802 - acc: 0.9777 - ETA: 4s - loss: 0.0828 - acc: 0.9768 - ETA: 4s - loss: 0.0859 - acc: 0.9760 - ETA: 4s - loss: 0.0867 - acc: 0.9752 - ETA: 4s - loss: 0.0865 - acc: 0.9755 - ETA: 4s - loss: 0.0864 - acc: 0.9752 - ETA: 4s - loss: 0.0868 - acc: 0.9746 - ETA: 4s - loss: 0.0875 - acc: 0.9743 - ETA: 4s - loss: 0.0871 - acc: 0.9742 - ETA: 4s - loss: 0.0877 - acc: 0.9744 - ETA: 4s - loss: 0.0875 - acc: 0.9742 - ETA: 4s - loss: 0.0888 - acc: 0.9740 - ETA: 4s - loss: 0.0916 - acc: 0.9738 - ETA: 4s - loss: 0.0900 - acc: 0.9744 - ETA: 3s - loss: 0.0927 - acc: 0.9739 - ETA: 3s - loss: 0.0922 - acc: 0.9737 - ETA: 3s - loss: 0.0939 - acc: 0.9729 - ETA: 3s - loss: 0.0924 - acc: 0.9734 - ETA: 3s - loss: 0.0909 - acc: 0.9740 - ETA: 3s - loss: 0.0903 - acc: 0.9742 - ETA: 3s - loss: 0.0895 - acc: 0.9740 - ETA: 3s - loss: 0.0882 - acc: 0.9745 - ETA: 3s - loss: 0.0891 - acc: 0.9744 - ETA: 3s - loss: 0.0898 - acc: 0.9739 - ETA: 3s - loss: 0.0904 - acc: 0.9738 - ETA: 3s - loss: 0.0893 - acc: 0.9743 - ETA: 3s - loss: 0.0923 - acc: 0.9738 - ETA: 3s - loss: 0.0919 - acc: 0.9737 - ETA: 3s - loss: 0.0910 - acc: 0.9739 - ETA: 3s - loss: 0.0948 - acc: 0.9729 - ETA: 3s - loss: 0.0951 - acc: 0.9726 - ETA: 2s - loss: 0.0950 - acc: 0.9725 - ETA: 2s - loss: 0.0984 - acc: 0.9724 - ETA: 2s - loss: 0.0977 - acc: 0.9725 - ETA: 2s - loss: 0.0977 - acc: 0.9724 - ETA: 2s - loss: 0.0966 - acc: 0.9726 - ETA: 2s - loss: 0.0973 - acc: 0.9720 - ETA: 2s - loss: 0.0968 - acc: 0.9722 - ETA: 2s - loss: 0.0959 - acc: 0.9724 - ETA: 2s - loss: 0.0950 - acc: 0.9725 - ETA: 2s - loss: 0.0959 - acc: 0.9717 - ETA: 2s - loss: 0.0962 - acc: 0.9714 - ETA: 2s - loss: 0.0950 - acc: 0.9718 - ETA: 2s - loss: 0.0945 - acc: 0.9717 - ETA: 2s - loss: 0.0936 - acc: 0.9719 - ETA: 2s - loss: 0.0927 - acc: 0.9723 - ETA: 2s - loss: 0.0920 - acc: 0.9726 - ETA: 2s - loss: 0.0927 - acc: 0.9721 - ETA: 1s - loss: 0.0937 - acc: 0.9716 - ETA: 1s - loss: 0.0929 - acc: 0.9718 - ETA: 1s - loss: 0.0941 - acc: 0.9713 - ETA: 1s - loss: 0.0933 - acc: 0.9717 - ETA: 1s - loss: 0.0957 - acc: 0.9714 - ETA: 1s - loss: 0.0950 - acc: 0.9717 - ETA: 1s - loss: 0.0948 - acc: 0.9719 - ETA: 1s - loss: 0.0956 - acc: 0.9716 - ETA: 1s - loss: 0.0947 - acc: 0.9719 - ETA: 1s - loss: 0.0952 - acc: 0.9719 - ETA: 1s - loss: 0.0948 - acc: 0.9716 - ETA: 1s - loss: 0.0941 - acc: 0.9718 - ETA: 1s - loss: 0.0935 - acc: 0.9719 - ETA: 1s - loss: 0.0937 - acc: 0.9717 - ETA: 1s - loss: 0.0944 - acc: 0.9714 - ETA: 1s - loss: 0.0937 - acc: 0.9717 - ETA: 0s - loss: 0.0944 - acc: 0.9710 - ETA: 0s - loss: 0.0941 - acc: 0.9711 - ETA: 0s - loss: 0.0961 - acc: 0.9705 - ETA: 0s - loss: 0.0967 - acc: 0.9702 - ETA: 0s - loss: 0.0975 - acc: 0.9698 - ETA: 0s - loss: 0.0966 - acc: 0.9701 - ETA: 0s - loss: 0.0960 - acc: 0.9704 - ETA: 0s - loss: 0.0982 - acc: 0.9702 - ETA: 0s - loss: 0.0996 - acc: 0.9697 - ETA: 0s - loss: 0.0990 - acc: 0.9698 - ETA: 0s - loss: 0.0984 - acc: 0.9701 - ETA: 0s - loss: 0.0976 - acc: 0.9704 - ETA: 0s - loss: 0.0971 - acc: 0.9705 - ETA: 0s - loss: 0.0986 - acc: 0.9705 - ETA: 0s - loss: 0.0985 - acc: 0.9704 - ETA: 0s - loss: 0.0985 - acc: 0.9702Epoch 00014: val_loss did not improve 6680/6680 [==============================] - 7s - loss: 0.0979 - acc: 0.9705 - val_loss: 0.6209 - val_acc: 0.8599 Epoch 16/20 6660/6680 [============================>.] - ETA: 6s - loss: 0.1650 - acc: 0.9500 - ETA: 6s - loss: 0.0590 - acc: 0.9875 - ETA: 6s - loss: 0.0965 - acc: 0.9786 - ETA: 6s - loss: 0.0832 - acc: 0.9750 - ETA: 6s - loss: 0.0791 - acc: 0.9769 - ETA: 6s - loss: 0.1104 - acc: 0.9719 - ETA: 6s - loss: 0.1056 - acc: 0.9737 - ETA: 6s - loss: 0.0971 - acc: 0.9750 - ETA: 6s - loss: 0.0941 - acc: 0.9760 - ETA: 6s - loss: 0.0888 - acc: 0.9768 - ETA: 6s - loss: 0.0815 - acc: 0.9790 - ETA: 6s - loss: 0.0804 - acc: 0.9779 - ETA: 6s - loss: 0.0767 - acc: 0.9784 - ETA: 6s - loss: 0.0774 - acc: 0.9775 - ETA: 6s - loss: 0.0730 - acc: 0.9791 - ETA: 6s - loss: 0.0783 - acc: 0.9793 - ETA: 6s - loss: 0.0788 - acc: 0.9796 - ETA: 6s - loss: 0.0754 - acc: 0.9808 - ETA: 5s - loss: 0.0805 - acc: 0.9782 - ETA: 5s - loss: 0.0793 - acc: 0.9784 - ETA: 5s - loss: 0.0818 - acc: 0.9770 - ETA: 5s - loss: 0.0806 - acc: 0.9766 - ETA: 5s - loss: 0.0833 - acc: 0.9761 - ETA: 5s - loss: 0.0816 - acc: 0.9771 - ETA: 5s - loss: 0.0863 - acc: 0.9767 - ETA: 5s - loss: 0.0840 - acc: 0.9770 - ETA: 5s - loss: 0.0843 - acc: 0.9766 - ETA: 5s - loss: 0.0851 - acc: 0.9762 - ETA: 5s - loss: 0.0881 - acc: 0.9759 - ETA: 5s - loss: 0.0871 - acc: 0.9761 - ETA: 5s - loss: 0.0899 - acc: 0.9742 - ETA: 5s - loss: 0.0894 - acc: 0.9734 - ETA: 5s - loss: 0.0876 - acc: 0.9737 - ETA: 5s - loss: 0.0865 - acc: 0.9742 - ETA: 4s - loss: 0.0860 - acc: 0.9745 - ETA: 4s - loss: 0.0924 - acc: 0.9743 - ETA: 4s - loss: 0.0911 - acc: 0.9750 - ETA: 4s - loss: 0.0892 - acc: 0.9757 - ETA: 4s - loss: 0.0881 - acc: 0.9754 - ETA: 4s - loss: 0.0924 - acc: 0.9744 - ETA: 4s - loss: 0.0910 - acc: 0.9746 - ETA: 4s - loss: 0.0916 - acc: 0.9748 - ETA: 4s - loss: 0.0908 - acc: 0.9746 - ETA: 4s - loss: 0.0889 - acc: 0.9752 - ETA: 4s - loss: 0.0879 - acc: 0.9750 - ETA: 4s - loss: 0.0894 - acc: 0.9752 - ETA: 4s - loss: 0.0880 - acc: 0.9754 - ETA: 4s - loss: 0.0876 - acc: 0.9755 - ETA: 4s - loss: 0.0882 - acc: 0.9750 - ETA: 3s - loss: 0.0894 - acc: 0.9741 - ETA: 3s - loss: 0.0889 - acc: 0.9743 - ETA: 3s - loss: 0.0891 - acc: 0.9739 - ETA: 3s - loss: 0.0884 - acc: 0.9744 - ETA: 3s - loss: 0.0874 - acc: 0.9745 - ETA: 3s - loss: 0.0866 - acc: 0.9747 - ETA: 3s - loss: 0.0860 - acc: 0.9748 - ETA: 3s - loss: 0.0854 - acc: 0.9750 - ETA: 3s - loss: 0.0857 - acc: 0.9751 - ETA: 3s - loss: 0.0848 - acc: 0.9756 - ETA: 3s - loss: 0.0858 - acc: 0.9751 - ETA: 3s - loss: 0.0845 - acc: 0.9756 - ETA: 3s - loss: 0.0843 - acc: 0.9754 - ETA: 3s - loss: 0.0841 - acc: 0.9753 - ETA: 3s - loss: 0.0829 - acc: 0.9757 - ETA: 2s - loss: 0.0827 - acc: 0.9755 - ETA: 2s - loss: 0.0826 - acc: 0.9754 - ETA: 2s - loss: 0.0818 - acc: 0.9755 - ETA: 2s - loss: 0.0810 - acc: 0.9756 - ETA: 2s - loss: 0.0842 - acc: 0.9755 - ETA: 2s - loss: 0.0834 - acc: 0.9754 - ETA: 2s - loss: 0.0853 - acc: 0.9752 - ETA: 2s - loss: 0.0844 - acc: 0.9756 - ETA: 2s - loss: 0.0841 - acc: 0.9755 - ETA: 2s - loss: 0.0850 - acc: 0.9753 - ETA: 2s - loss: 0.0848 - acc: 0.9752 - ETA: 2s - loss: 0.0838 - acc: 0.9756 - ETA: 2s - loss: 0.0831 - acc: 0.9757 - ETA: 2s - loss: 0.0835 - acc: 0.9753 - ETA: 2s - loss: 0.0834 - acc: 0.9754 - ETA: 2s - loss: 0.0845 - acc: 0.9749 - ETA: 1s - loss: 0.0840 - acc: 0.9750 - ETA: 1s - loss: 0.0839 - acc: 0.9751 - ETA: 1s - loss: 0.0847 - acc: 0.9748 - ETA: 1s - loss: 0.0852 - acc: 0.9743 - ETA: 1s - loss: 0.0866 - acc: 0.9740 - ETA: 1s - loss: 0.0870 - acc: 0.9739 - ETA: 1s - loss: 0.0870 - acc: 0.9738 - ETA: 1s - loss: 0.0865 - acc: 0.9739 - ETA: 1s - loss: 0.0875 - acc: 0.9733 - ETA: 1s - loss: 0.0874 - acc: 0.9734 - ETA: 1s - loss: 0.0885 - acc: 0.9730 - ETA: 1s - loss: 0.0879 - acc: 0.9733 - ETA: 1s - loss: 0.0877 - acc: 0.9734 - ETA: 1s - loss: 0.0869 - acc: 0.9737 - ETA: 1s - loss: 0.0863 - acc: 0.9739 - ETA: 1s - loss: 0.0858 - acc: 0.9740 - ETA: 0s - loss: 0.0862 - acc: 0.9740 - ETA: 0s - loss: 0.0858 - acc: 0.9741 - ETA: 0s - loss: 0.0864 - acc: 0.9738 - ETA: 0s - loss: 0.0875 - acc: 0.9736 - ETA: 0s - loss: 0.0872 - acc: 0.9737 - ETA: 0s - loss: 0.0876 - acc: 0.9736 - ETA: 0s - loss: 0.0873 - acc: 0.9735 - ETA: 0s - loss: 0.0885 - acc: 0.9733 - ETA: 0s - loss: 0.0908 - acc: 0.9728 - ETA: 0s - loss: 0.0918 - acc: 0.9727 - ETA: 0s - loss: 0.0916 - acc: 0.9726 - ETA: 0s - loss: 0.0912 - acc: 0.9727 - ETA: 0s - loss: 0.0909 - acc: 0.9728 - ETA: 0s - loss: 0.0902 - acc: 0.9731 - ETA: 0s - loss: 0.0916 - acc: 0.9730 - ETA: 0s - loss: 0.0923 - acc: 0.9728Epoch 00015: val_loss did not improve 6680/6680 [==============================] - 7s - loss: 0.0921 - acc: 0.9729 - val_loss: 0.6234 - val_acc: 0.8635 Epoch 17/20 6620/6680 [============================>.] - ETA: 6s - loss: 0.2965 - acc: 0.9500 - ETA: 6s - loss: 0.1345 - acc: 0.9625 - ETA: 6s - loss: 0.1052 - acc: 0.9643 - ETA: 6s - loss: 0.0859 - acc: 0.9650 - ETA: 6s - loss: 0.0742 - acc: 0.9654 - ETA: 6s - loss: 0.0642 - acc: 0.9687 - ETA: 6s - loss: 0.0588 - acc: 0.9737 - ETA: 6s - loss: 0.0668 - acc: 0.9727 - ETA: 6s - loss: 0.0614 - acc: 0.9760 - ETA: 6s - loss: 0.0608 - acc: 0.9768 - ETA: 6s - loss: 0.0556 - acc: 0.9790 - ETA: 6s - loss: 0.0528 - acc: 0.9809 - ETA: 6s - loss: 0.0501 - acc: 0.9824 - ETA: 6s - loss: 0.0485 - acc: 0.9825 - ETA: 6s - loss: 0.0619 - acc: 0.9779 - ETA: 6s - loss: 0.0713 - acc: 0.9761 - ETA: 6s - loss: 0.0750 - acc: 0.9755 - ETA: 5s - loss: 0.0737 - acc: 0.9750 - ETA: 5s - loss: 0.0759 - acc: 0.9745 - ETA: 5s - loss: 0.0837 - acc: 0.9741 - ETA: 5s - loss: 0.0806 - acc: 0.9754 - ETA: 5s - loss: 0.0795 - acc: 0.9750 - ETA: 5s - loss: 0.0789 - acc: 0.9754 - ETA: 5s - loss: 0.0778 - acc: 0.9757 - ETA: 5s - loss: 0.0849 - acc: 0.9747 - ETA: 5s - loss: 0.0851 - acc: 0.9750 - ETA: 5s - loss: 0.0825 - acc: 0.9759 - ETA: 5s - loss: 0.0798 - acc: 0.9768 - ETA: 5s - loss: 0.0790 - acc: 0.9771 - ETA: 5s - loss: 0.0795 - acc: 0.9773 - ETA: 5s - loss: 0.0782 - acc: 0.9775 - ETA: 5s - loss: 0.0764 - acc: 0.9777 - ETA: 5s - loss: 0.0822 - acc: 0.9768 - ETA: 4s - loss: 0.0843 - acc: 0.9755 - ETA: 4s - loss: 0.0931 - acc: 0.9743 - ETA: 4s - loss: 0.0908 - acc: 0.9750 - ETA: 4s - loss: 0.0891 - acc: 0.9757 - ETA: 4s - loss: 0.0876 - acc: 0.9759 - ETA: 4s - loss: 0.0870 - acc: 0.9757 - ETA: 4s - loss: 0.0855 - acc: 0.9763 - ETA: 4s - loss: 0.0846 - acc: 0.9764 - ETA: 4s - loss: 0.0836 - acc: 0.9762 - ETA: 4s - loss: 0.0853 - acc: 0.9752 - ETA: 4s - loss: 0.0841 - acc: 0.9754 - ETA: 4s - loss: 0.0839 - acc: 0.9748 - ETA: 4s - loss: 0.0835 - acc: 0.9750 - ETA: 4s - loss: 0.0837 - acc: 0.9748 - ETA: 4s - loss: 0.0824 - acc: 0.9754 - ETA: 4s - loss: 0.0846 - acc: 0.9748 - ETA: 3s - loss: 0.0861 - acc: 0.9750 - ETA: 3s - loss: 0.0861 - acc: 0.9748 - ETA: 3s - loss: 0.0855 - acc: 0.9750 - ETA: 3s - loss: 0.0877 - acc: 0.9742 - ETA: 3s - loss: 0.0891 - acc: 0.9737 - ETA: 3s - loss: 0.0879 - acc: 0.9739 - ETA: 3s - loss: 0.0875 - acc: 0.9741 - ETA: 3s - loss: 0.0863 - acc: 0.9746 - ETA: 3s - loss: 0.0860 - acc: 0.9747 - ETA: 3s - loss: 0.0860 - acc: 0.9743 - ETA: 3s - loss: 0.0856 - acc: 0.9744 - ETA: 3s - loss: 0.0845 - acc: 0.9749 - ETA: 3s - loss: 0.0834 - acc: 0.9753 - ETA: 3s - loss: 0.0852 - acc: 0.9746 - ETA: 3s - loss: 0.0840 - acc: 0.9750 - ETA: 3s - loss: 0.0829 - acc: 0.9754 - ETA: 2s - loss: 0.0821 - acc: 0.9755 - ETA: 2s - loss: 0.0809 - acc: 0.9759 - ETA: 2s - loss: 0.0824 - acc: 0.9757 - ETA: 2s - loss: 0.0858 - acc: 0.9751 - ETA: 2s - loss: 0.0855 - acc: 0.9748 - ETA: 2s - loss: 0.0852 - acc: 0.9746 - ETA: 2s - loss: 0.0858 - acc: 0.9743 - ETA: 2s - loss: 0.0850 - acc: 0.9744 - ETA: 2s - loss: 0.0855 - acc: 0.9741 - ETA: 2s - loss: 0.0866 - acc: 0.9738 - ETA: 2s - loss: 0.0868 - acc: 0.9739 - ETA: 2s - loss: 0.0869 - acc: 0.9738 - ETA: 2s - loss: 0.0915 - acc: 0.9733 - ETA: 2s - loss: 0.0923 - acc: 0.9730 - ETA: 2s - loss: 0.0924 - acc: 0.9729 - ETA: 1s - loss: 0.0921 - acc: 0.9730 - ETA: 1s - loss: 0.0918 - acc: 0.9732 - ETA: 1s - loss: 0.0910 - acc: 0.9733 - ETA: 1s - loss: 0.0931 - acc: 0.9726 - ETA: 1s - loss: 0.0926 - acc: 0.9725 - ETA: 1s - loss: 0.0931 - acc: 0.9725 - ETA: 1s - loss: 0.0926 - acc: 0.9726 - ETA: 1s - loss: 0.0927 - acc: 0.9725 - ETA: 1s - loss: 0.0955 - acc: 0.9721 - ETA: 1s - loss: 0.0953 - acc: 0.9718 - ETA: 1s - loss: 0.0945 - acc: 0.9721 - ETA: 1s - loss: 0.0939 - acc: 0.9723 - ETA: 1s - loss: 0.0932 - acc: 0.9722 - ETA: 1s - loss: 0.0938 - acc: 0.9720 - ETA: 1s - loss: 0.0933 - acc: 0.9721 - ETA: 1s - loss: 0.0924 - acc: 0.9724 - ETA: 0s - loss: 0.0928 - acc: 0.9723 - ETA: 0s - loss: 0.0919 - acc: 0.9726 - ETA: 0s - loss: 0.0921 - acc: 0.9725 - ETA: 0s - loss: 0.0922 - acc: 0.9723 - ETA: 0s - loss: 0.0914 - acc: 0.9726 - ETA: 0s - loss: 0.0925 - acc: 0.9722 - ETA: 0s - loss: 0.0921 - acc: 0.9721 - ETA: 0s - loss: 0.0915 - acc: 0.9724 - ETA: 0s - loss: 0.0919 - acc: 0.9724 - ETA: 0s - loss: 0.0913 - acc: 0.9726 - ETA: 0s - loss: 0.0906 - acc: 0.9727 - ETA: 0s - loss: 0.0908 - acc: 0.9727 - ETA: 0s - loss: 0.0901 - acc: 0.9728 - ETA: 0s - loss: 0.0899 - acc: 0.9729 - ETA: 0s - loss: 0.0896 - acc: 0.9730Epoch 00016: val_loss did not improve 6680/6680 [==============================] - 7s - loss: 0.0898 - acc: 0.9731 - val_loss: 0.6858 - val_acc: 0.8455 Epoch 18/20 6660/6680 [============================>.] - ETA: 6s - loss: 0.0013 - acc: 1.0000 - ETA: 6s - loss: 0.0212 - acc: 1.0000 - ETA: 6s - loss: 0.0408 - acc: 0.9857 - ETA: 6s - loss: 0.0702 - acc: 0.9800 - ETA: 6s - loss: 0.0673 - acc: 0.9769 - ETA: 6s - loss: 0.0589 - acc: 0.9812 - ETA: 6s - loss: 0.0584 - acc: 0.9816 - ETA: 6s - loss: 0.0543 - acc: 0.9818 - ETA: 6s - loss: 0.0493 - acc: 0.9840 - ETA: 6s - loss: 0.0462 - acc: 0.9857 - ETA: 6s - loss: 0.0502 - acc: 0.9855 - ETA: 6s - loss: 0.0468 - acc: 0.9868 - ETA: 5s - loss: 0.0458 - acc: 0.9865 - ETA: 5s - loss: 0.0432 - acc: 0.9875 - ETA: 5s - loss: 0.0437 - acc: 0.9872 - ETA: 5s - loss: 0.0429 - acc: 0.9870 - ETA: 5s - loss: 0.0432 - acc: 0.9867 - ETA: 5s - loss: 0.0447 - acc: 0.9865 - ETA: 5s - loss: 0.0459 - acc: 0.9864 - ETA: 5s - loss: 0.0485 - acc: 0.9862 - ETA: 5s - loss: 0.0491 - acc: 0.9852 - ETA: 5s - loss: 0.0518 - acc: 0.9852 - ETA: 5s - loss: 0.0560 - acc: 0.9828 - ETA: 5s - loss: 0.0548 - acc: 0.9836 - ETA: 5s - loss: 0.0540 - acc: 0.9836 - ETA: 5s - loss: 0.0529 - acc: 0.9836 - ETA: 5s - loss: 0.0541 - acc: 0.9835 - ETA: 5s - loss: 0.0542 - acc: 0.9829 - ETA: 5s - loss: 0.0548 - acc: 0.9818 - ETA: 4s - loss: 0.0573 - acc: 0.9818 - ETA: 4s - loss: 0.0581 - acc: 0.9813 - ETA: 4s - loss: 0.0565 - acc: 0.9819 - ETA: 4s - loss: 0.0556 - acc: 0.9825 - ETA: 4s - loss: 0.0554 - acc: 0.9825 - ETA: 4s - loss: 0.0563 - acc: 0.9825 - ETA: 4s - loss: 0.0599 - acc: 0.9821 - ETA: 4s - loss: 0.0602 - acc: 0.9821 - ETA: 4s - loss: 0.0604 - acc: 0.9817 - ETA: 4s - loss: 0.0600 - acc: 0.9817 - ETA: 4s - loss: 0.0603 - acc: 0.9814 - ETA: 4s - loss: 0.0622 - acc: 0.9806 - ETA: 4s - loss: 0.0660 - acc: 0.9806 - ETA: 4s - loss: 0.0691 - acc: 0.9799 - ETA: 4s - loss: 0.0690 - acc: 0.9800 - ETA: 4s - loss: 0.0706 - acc: 0.9801 - ETA: 3s - loss: 0.0724 - acc: 0.9798 - ETA: 3s - loss: 0.0724 - acc: 0.9799 - ETA: 3s - loss: 0.0741 - acc: 0.9796 - ETA: 3s - loss: 0.0739 - acc: 0.9797 - ETA: 3s - loss: 0.0729 - acc: 0.9801 - ETA: 3s - loss: 0.0732 - acc: 0.9798 - ETA: 3s - loss: 0.0752 - acc: 0.9786 - ETA: 3s - loss: 0.0744 - acc: 0.9787 - ETA: 3s - loss: 0.0735 - acc: 0.9791 - ETA: 3s - loss: 0.0723 - acc: 0.9794 - ETA: 3s - loss: 0.0729 - acc: 0.9789 - ETA: 3s - loss: 0.0721 - acc: 0.9790 - ETA: 3s - loss: 0.0715 - acc: 0.9791 - ETA: 3s - loss: 0.0727 - acc: 0.9791 - ETA: 3s - loss: 0.0742 - acc: 0.9792 - ETA: 3s - loss: 0.0731 - acc: 0.9796 - ETA: 3s - loss: 0.0723 - acc: 0.9796 - ETA: 2s - loss: 0.0716 - acc: 0.9797 - ETA: 2s - loss: 0.0712 - acc: 0.9797 - ETA: 2s - loss: 0.0704 - acc: 0.9801 - ETA: 2s - loss: 0.0720 - acc: 0.9798 - ETA: 2s - loss: 0.0724 - acc: 0.9796 - ETA: 2s - loss: 0.0734 - acc: 0.9790 - ETA: 2s - loss: 0.0731 - acc: 0.9788 - ETA: 2s - loss: 0.0724 - acc: 0.9788 - ETA: 2s - loss: 0.0734 - acc: 0.9784 - ETA: 2s - loss: 0.0729 - acc: 0.9783 - ETA: 2s - loss: 0.0720 - acc: 0.9786 - ETA: 2s - loss: 0.0718 - acc: 0.9786 - ETA: 2s - loss: 0.0751 - acc: 0.9780 - ETA: 2s - loss: 0.0756 - acc: 0.9774 - ETA: 2s - loss: 0.0769 - acc: 0.9773 - ETA: 2s - loss: 0.0762 - acc: 0.9776 - ETA: 2s - loss: 0.0766 - acc: 0.9774 - ETA: 1s - loss: 0.0761 - acc: 0.9775 - ETA: 1s - loss: 0.0756 - acc: 0.9776 - ETA: 1s - loss: 0.0749 - acc: 0.9779 - ETA: 1s - loss: 0.0744 - acc: 0.9779 - ETA: 1s - loss: 0.0767 - acc: 0.9774 - ETA: 1s - loss: 0.0774 - acc: 0.9769 - ETA: 1s - loss: 0.0776 - acc: 0.9768 - ETA: 1s - loss: 0.0781 - acc: 0.9763 - ETA: 1s - loss: 0.0774 - acc: 0.9765 - ETA: 1s - loss: 0.0768 - acc: 0.9768 - ETA: 1s - loss: 0.0817 - acc: 0.9761 - ETA: 1s - loss: 0.0813 - acc: 0.9762 - ETA: 1s - loss: 0.0844 - acc: 0.9755 - ETA: 1s - loss: 0.0845 - acc: 0.9755 - ETA: 1s - loss: 0.0840 - acc: 0.9757 - ETA: 1s - loss: 0.0846 - acc: 0.9754 - ETA: 1s - loss: 0.0847 - acc: 0.9755 - ETA: 0s - loss: 0.0841 - acc: 0.9758 - ETA: 0s - loss: 0.0835 - acc: 0.9760 - ETA: 0s - loss: 0.0828 - acc: 0.9763 - ETA: 0s - loss: 0.0821 - acc: 0.9765 - ETA: 0s - loss: 0.0816 - acc: 0.9768 - ETA: 0s - loss: 0.0812 - acc: 0.9768 - ETA: 0s - loss: 0.0827 - acc: 0.9769 - ETA: 0s - loss: 0.0821 - acc: 0.9771 - ETA: 0s - loss: 0.0834 - acc: 0.9767 - ETA: 0s - loss: 0.0830 - acc: 0.9768 - ETA: 0s - loss: 0.0834 - acc: 0.9763 - ETA: 0s - loss: 0.0830 - acc: 0.9762 - ETA: 0s - loss: 0.0830 - acc: 0.9762 - ETA: 0s - loss: 0.0831 - acc: 0.9762 - ETA: 0s - loss: 0.0833 - acc: 0.9760 - ETA: 0s - loss: 0.0830 - acc: 0.9761 - ETA: 0s - loss: 0.0828 - acc: 0.9761 - ETA: 0s - loss: 0.0825 - acc: 0.9761Epoch 00017: val_loss did not improve 6680/6680 [==============================] - 7s - loss: 0.0823 - acc: 0.9762 - val_loss: 0.6616 - val_acc: 0.8527 Epoch 19/20 6620/6680 [============================>.] - ETA: 6s - loss: 0.0019 - acc: 1.0000 - ETA: 6s - loss: 0.0174 - acc: 0.9875 - ETA: 6s - loss: 0.0192 - acc: 0.9929 - ETA: 6s - loss: 0.0262 - acc: 0.9900 - ETA: 6s - loss: 0.0610 - acc: 0.9731 - ETA: 6s - loss: 0.0690 - acc: 0.9750 - ETA: 6s - loss: 0.0730 - acc: 0.9711 - ETA: 6s - loss: 0.0747 - acc: 0.9705 - ETA: 6s - loss: 0.0776 - acc: 0.9720 - ETA: 6s - loss: 0.0742 - acc: 0.9732 - ETA: 6s - loss: 0.0690 - acc: 0.9742 - ETA: 6s - loss: 0.0634 - acc: 0.9765 - ETA: 6s - loss: 0.0733 - acc: 0.9743 - ETA: 6s - loss: 0.0706 - acc: 0.9750 - ETA: 6s - loss: 0.0672 - acc: 0.9767 - ETA: 6s - loss: 0.0702 - acc: 0.9772 - ETA: 6s - loss: 0.0670 - acc: 0.9786 - ETA: 6s - loss: 0.0700 - acc: 0.9769 - ETA: 5s - loss: 0.0689 - acc: 0.9773 - ETA: 5s - loss: 0.0664 - acc: 0.9784 - ETA: 5s - loss: 0.0640 - acc: 0.9795 - ETA: 5s - loss: 0.0616 - acc: 0.9805 - ETA: 5s - loss: 0.0622 - acc: 0.9799 - ETA: 5s - loss: 0.0607 - acc: 0.9800 - ETA: 5s - loss: 0.0622 - acc: 0.9788 - ETA: 5s - loss: 0.0642 - acc: 0.9783 - ETA: 5s - loss: 0.0625 - acc: 0.9785 - ETA: 5s - loss: 0.0677 - acc: 0.9774 - ETA: 5s - loss: 0.0732 - acc: 0.9771 - ETA: 5s - loss: 0.0740 - acc: 0.9773 - ETA: 5s - loss: 0.0742 - acc: 0.9769 - ETA: 5s - loss: 0.0721 - acc: 0.9777 - ETA: 5s - loss: 0.0718 - acc: 0.9773 - ETA: 5s - loss: 0.0728 - acc: 0.9765 - ETA: 4s - loss: 0.0713 - acc: 0.9767 - ETA: 4s - loss: 0.0695 - acc: 0.9774 - ETA: 4s - loss: 0.0720 - acc: 0.9775 - ETA: 4s - loss: 0.0704 - acc: 0.9781 - ETA: 4s - loss: 0.0699 - acc: 0.9778 - ETA: 4s - loss: 0.0709 - acc: 0.9775 - ETA: 4s - loss: 0.0710 - acc: 0.9773 - ETA: 4s - loss: 0.0696 - acc: 0.9778 - ETA: 4s - loss: 0.0730 - acc: 0.9768 - ETA: 4s - loss: 0.0733 - acc: 0.9762 - ETA: 4s - loss: 0.0723 - acc: 0.9767 - ETA: 4s - loss: 0.0721 - acc: 0.9765 - ETA: 4s - loss: 0.0742 - acc: 0.9763 - ETA: 4s - loss: 0.0731 - acc: 0.9768 - ETA: 4s - loss: 0.0734 - acc: 0.9766 - ETA: 3s - loss: 0.0728 - acc: 0.9767 - ETA: 3s - loss: 0.0722 - acc: 0.9768 - ETA: 3s - loss: 0.0724 - acc: 0.9769 - ETA: 3s - loss: 0.0718 - acc: 0.9771 - ETA: 3s - loss: 0.0765 - acc: 0.9769 - ETA: 3s - loss: 0.0755 - acc: 0.9770 - ETA: 3s - loss: 0.0754 - acc: 0.9771 - ETA: 3s - loss: 0.0743 - acc: 0.9775 - ETA: 3s - loss: 0.0785 - acc: 0.9773 - ETA: 3s - loss: 0.0775 - acc: 0.9777 - ETA: 3s - loss: 0.0766 - acc: 0.9778 - ETA: 3s - loss: 0.0791 - acc: 0.9776 - ETA: 3s - loss: 0.0791 - acc: 0.9777 - ETA: 3s - loss: 0.0790 - acc: 0.9775 - ETA: 3s - loss: 0.0799 - acc: 0.9774 - ETA: 3s - loss: 0.0798 - acc: 0.9775 - ETA: 2s - loss: 0.0800 - acc: 0.9770 - ETA: 2s - loss: 0.0797 - acc: 0.9771 - ETA: 2s - loss: 0.0790 - acc: 0.9775 - ETA: 2s - loss: 0.0801 - acc: 0.9773 - ETA: 2s - loss: 0.0792 - acc: 0.9776 - ETA: 2s - loss: 0.0794 - acc: 0.9770 - ETA: 2s - loss: 0.0784 - acc: 0.9773 - ETA: 2s - loss: 0.0786 - acc: 0.9772 - ETA: 2s - loss: 0.0777 - acc: 0.9775 - ETA: 2s - loss: 0.0770 - acc: 0.9778 - ETA: 2s - loss: 0.0766 - acc: 0.9779 - ETA: 2s - loss: 0.0764 - acc: 0.9777 - ETA: 2s - loss: 0.0773 - acc: 0.9778 - ETA: 2s - loss: 0.0771 - acc: 0.9779 - ETA: 2s - loss: 0.0763 - acc: 0.9782 - ETA: 1s - loss: 0.0780 - acc: 0.9778 - ETA: 1s - loss: 0.0775 - acc: 0.9779 - ETA: 1s - loss: 0.0779 - acc: 0.9775 - ETA: 1s - loss: 0.0771 - acc: 0.9778 - ETA: 1s - loss: 0.0767 - acc: 0.9779 - ETA: 1s - loss: 0.0758 - acc: 0.9781 - ETA: 1s - loss: 0.0755 - acc: 0.9780 - ETA: 1s - loss: 0.0747 - acc: 0.9782 - ETA: 1s - loss: 0.0752 - acc: 0.9781 - ETA: 1s - loss: 0.0752 - acc: 0.9780 - ETA: 1s - loss: 0.0745 - acc: 0.9782 - ETA: 1s - loss: 0.0743 - acc: 0.9781 - ETA: 1s - loss: 0.0742 - acc: 0.9780 - ETA: 1s - loss: 0.0752 - acc: 0.9779 - ETA: 1s - loss: 0.0746 - acc: 0.9781 - ETA: 1s - loss: 0.0751 - acc: 0.9778 - ETA: 0s - loss: 0.0754 - acc: 0.9779 - ETA: 0s - loss: 0.0763 - acc: 0.9776 - ETA: 0s - loss: 0.0762 - acc: 0.9773 - ETA: 0s - loss: 0.0769 - acc: 0.9770 - ETA: 0s - loss: 0.0765 - acc: 0.9771 - ETA: 0s - loss: 0.0761 - acc: 0.9771 - ETA: 0s - loss: 0.0755 - acc: 0.9774 - ETA: 0s - loss: 0.0766 - acc: 0.9771 - ETA: 0s - loss: 0.0762 - acc: 0.9773 - ETA: 0s - loss: 0.0763 - acc: 0.9771 - ETA: 0s - loss: 0.0781 - acc: 0.9766 - ETA: 0s - loss: 0.0774 - acc: 0.9769 - ETA: 0s - loss: 0.0780 - acc: 0.9769 - ETA: 0s - loss: 0.0774 - acc: 0.9771 - ETA: 0s - loss: 0.0771 - acc: 0.9772Epoch 00018: val_loss did not improve 6680/6680 [==============================] - 7s - loss: 0.0771 - acc: 0.9772 - val_loss: 0.6721 - val_acc: 0.8575 Epoch 20/20 6620/6680 [============================>.] - ETA: 6s - loss: 0.1905 - acc: 0.8500 - ETA: 6s - loss: 0.0562 - acc: 0.9625 - ETA: 6s - loss: 0.0356 - acc: 0.9786 - ETA: 6s - loss: 0.0276 - acc: 0.9850 - ETA: 6s - loss: 0.0380 - acc: 0.9846 - ETA: 6s - loss: 0.0459 - acc: 0.9781 - ETA: 6s - loss: 0.0394 - acc: 0.9816 - ETA: 6s - loss: 0.0405 - acc: 0.9818 - ETA: 6s - loss: 0.0559 - acc: 0.9800 - ETA: 6s - loss: 0.0733 - acc: 0.9786 - ETA: 6s - loss: 0.0707 - acc: 0.9790 - ETA: 6s - loss: 0.0657 - acc: 0.9809 - ETA: 5s - loss: 0.0625 - acc: 0.9824 - ETA: 5s - loss: 0.0598 - acc: 0.9837 - ETA: 5s - loss: 0.0567 - acc: 0.9849 - ETA: 5s - loss: 0.0669 - acc: 0.9837 - ETA: 5s - loss: 0.0645 - acc: 0.9847 - ETA: 5s - loss: 0.0616 - acc: 0.9856 - ETA: 5s - loss: 0.0609 - acc: 0.9855 - ETA: 5s - loss: 0.0590 - acc: 0.9853 - ETA: 5s - loss: 0.0590 - acc: 0.9852 - ETA: 5s - loss: 0.0579 - acc: 0.9852 - ETA: 5s - loss: 0.0567 - acc: 0.9851 - ETA: 5s - loss: 0.0548 - acc: 0.9857 - ETA: 5s - loss: 0.0550 - acc: 0.9849 - ETA: 5s - loss: 0.0540 - acc: 0.9849 - ETA: 5s - loss: 0.0536 - acc: 0.9848 - ETA: 5s - loss: 0.0519 - acc: 0.9854 - ETA: 5s - loss: 0.0545 - acc: 0.9841 - ETA: 4s - loss: 0.0533 - acc: 0.9847 - ETA: 4s - loss: 0.0521 - acc: 0.9852 - ETA: 4s - loss: 0.0528 - acc: 0.9851 - ETA: 4s - loss: 0.0537 - acc: 0.9845 - ETA: 4s - loss: 0.0532 - acc: 0.9845 - ETA: 4s - loss: 0.0557 - acc: 0.9835 - ETA: 4s - loss: 0.0545 - acc: 0.9840 - ETA: 4s - loss: 0.0563 - acc: 0.9835 - ETA: 4s - loss: 0.0554 - acc: 0.9835 - ETA: 4s - loss: 0.0611 - acc: 0.9822 - ETA: 4s - loss: 0.0613 - acc: 0.9818 - ETA: 4s - loss: 0.0600 - acc: 0.9822 - ETA: 4s - loss: 0.0607 - acc: 0.9819 - ETA: 4s - loss: 0.0619 - acc: 0.9811 - ETA: 4s - loss: 0.0615 - acc: 0.9808 - ETA: 4s - loss: 0.0606 - acc: 0.9812 - ETA: 3s - loss: 0.0598 - acc: 0.9816 - ETA: 3s - loss: 0.0607 - acc: 0.9817 - ETA: 3s - loss: 0.0621 - acc: 0.9813 - ETA: 3s - loss: 0.0619 - acc: 0.9814 - ETA: 3s - loss: 0.0612 - acc: 0.9818 - ETA: 3s - loss: 0.0612 - acc: 0.9815 - ETA: 3s - loss: 0.0616 - acc: 0.9808 - ETA: 3s - loss: 0.0640 - acc: 0.9796 - ETA: 3s - loss: 0.0652 - acc: 0.9787 - ETA: 3s - loss: 0.0655 - acc: 0.9785 - ETA: 3s - loss: 0.0653 - acc: 0.9783 - ETA: 3s - loss: 0.0656 - acc: 0.9781 - ETA: 3s - loss: 0.0669 - acc: 0.9782 - ETA: 3s - loss: 0.0680 - acc: 0.9780 - ETA: 3s - loss: 0.0714 - acc: 0.9770 - ETA: 3s - loss: 0.0711 - acc: 0.9768 - ETA: 3s - loss: 0.0700 - acc: 0.9772 - ETA: 2s - loss: 0.0705 - acc: 0.9770 - ETA: 2s - loss: 0.0714 - acc: 0.9768 - ETA: 2s - loss: 0.0724 - acc: 0.9769 - ETA: 2s - loss: 0.0731 - acc: 0.9768 - ETA: 2s - loss: 0.0727 - acc: 0.9766 - ETA: 2s - loss: 0.0717 - acc: 0.9770 - ETA: 2s - loss: 0.0718 - acc: 0.9771 - ETA: 2s - loss: 0.0755 - acc: 0.9769 - ETA: 2s - loss: 0.0756 - acc: 0.9768 - ETA: 2s - loss: 0.0749 - acc: 0.9771 - ETA: 2s - loss: 0.0756 - acc: 0.9772 - ETA: 2s - loss: 0.0751 - acc: 0.9775 - ETA: 2s - loss: 0.0746 - acc: 0.9774 - ETA: 2s - loss: 0.0747 - acc: 0.9772 - ETA: 2s - loss: 0.0749 - acc: 0.9773 - ETA: 2s - loss: 0.0765 - acc: 0.9769 - ETA: 2s - loss: 0.0795 - acc: 0.9764 - ETA: 1s - loss: 0.0788 - acc: 0.9767 - ETA: 1s - loss: 0.0780 - acc: 0.9770 - ETA: 1s - loss: 0.0772 - acc: 0.9773 - ETA: 1s - loss: 0.0768 - acc: 0.9773 - ETA: 1s - loss: 0.0767 - acc: 0.9774 - ETA: 1s - loss: 0.0762 - acc: 0.9775 - ETA: 1s - loss: 0.0762 - acc: 0.9771 - ETA: 1s - loss: 0.0758 - acc: 0.9772 - ETA: 1s - loss: 0.0752 - acc: 0.9775 - ETA: 1s - loss: 0.0747 - acc: 0.9775 - ETA: 1s - loss: 0.0750 - acc: 0.9772 - ETA: 1s - loss: 0.0743 - acc: 0.9775 - ETA: 1s - loss: 0.0738 - acc: 0.9777 - ETA: 1s - loss: 0.0737 - acc: 0.9778 - ETA: 1s - loss: 0.0736 - acc: 0.9777 - ETA: 1s - loss: 0.0734 - acc: 0.9776 - ETA: 0s - loss: 0.0747 - acc: 0.9773 - ETA: 0s - loss: 0.0744 - acc: 0.9773 - ETA: 0s - loss: 0.0738 - acc: 0.9776 - ETA: 0s - loss: 0.0756 - acc: 0.9775 - ETA: 0s - loss: 0.0751 - acc: 0.9775 - ETA: 0s - loss: 0.0745 - acc: 0.9777 - ETA: 0s - loss: 0.0738 - acc: 0.9780 - ETA: 0s - loss: 0.0742 - acc: 0.9779 - ETA: 0s - loss: 0.0738 - acc: 0.9781 - ETA: 0s - loss: 0.0733 - acc: 0.9781 - ETA: 0s - loss: 0.0731 - acc: 0.9782 - ETA: 0s - loss: 0.0727 - acc: 0.9782 - ETA: 0s - loss: 0.0723 - acc: 0.9784 - ETA: 0s - loss: 0.0728 - acc: 0.9783 - ETA: 0s - loss: 0.0743 - acc: 0.9782 - ETA: 0s - loss: 0.0738 - acc: 0.9784Epoch 00019: val_loss did not improve 6680/6680 [==============================] - 7s - loss: 0.0734 - acc: 0.9784 - val_loss: 0.7048 - val_acc: 0.8599 ---I am done saving model valid_Xception ----
### TODO: Load the model weights with the best validation loss.
# valid_Xception loss: 0.0713 - acc: 0.9798 - val_loss: 0.6841 - val_acc: 0.8503 'weights.best.Xception.hdf5'
# valid_InceptionV3 loss: 0.0302 - acc: 0.9906 - val_loss: 0.9732 - val_acc: 0.8383 'weights.best.InceptionV3.hdf5'
# valid_Resnet50 loss: 0.0065 - acc: 0.9988 - val_loss: 0.9301 - val_acc: 0.8323 'weights.best.Resnet50.hdf5'
# VGG1 loss: 7.5263 - acc: 0.5213 - val_loss: 8.1408 - val_acc: 0.4311 'weights.best.VGG19.hdf5'
VGG19_model.load_weights('weights.best.VGG19.hdf5')
print('-- VGG19 Weights Loaded --- ')
Xception_model.load_weights('weights.best.Xception.hdf5')
print('-- Xception Weights Loaded --- ')
InceptionV3_model.load_weights('weights.best.InceptionV3.hdf5')
print('-- InceptionV3 Weights Loaded --- ')
Resnet50_model.load_weights('weights.best.Resnet50.hdf5')
print('-- Resnet50 Weights Loaded --- ')
-- VGG19 Weights Loaded --- -- Xception Weights Loaded --- -- InceptionV3 Weights Loaded --- -- Resnet50 Weights Loaded ---
Try out your model on the test dataset of dog images. Ensure that your test accuracy is greater than 60%.
### TODO: Calculate classification accuracy on the test dataset.
VGG19_predictions = [np.argmax(VGG19_model.predict(np.expand_dims(feature, axis=0))) for feature in test_VGG19]
print('-- VGG19_predictions Loaded --- ')
Xception_predictions = [np.argmax(Xception_model.predict(np.expand_dims(feature, axis=0))) for feature in test_Xception]
print('-- Xception_predictions Loaded --- ')
InceptionV3_predictions = [np.argmax(InceptionV3_model.predict(np.expand_dims(feature, axis=0))) for feature in test_InceptionV3]
print('-- InceptionV3_predictions Loaded --- ')
Resnet50_predictions = [np.argmax(Resnet50_model.predict(np.expand_dims(feature, axis=0))) for feature in test_Resnet50]
print('-- Resnet50_predictions Loaded --- ')
test_accuracy_VGG19 = 100*np.sum(np.array(VGG19_predictions)==np.argmax(test_targets, axis=1))/len(VGG19_predictions)
print('VGG19 Test accuracy: %.4f%%' % test_accuracy_VGG19)
test_accuracy_Xception = 100*np.sum(np.array(Xception_predictions)==np.argmax(test_targets, axis=1))/len(Xception_predictions)
print('Xception Test accuracy: %.4f%%' % test_accuracy_Xception)
test_accuracy_InceptionV3 = 100*np.sum(np.array(InceptionV3_predictions)==np.argmax(test_targets, axis=1))/len(InceptionV3_predictions)
print('Inception Test accuracy: %.4f%%' % test_accuracy_InceptionV3)
test_accuracy_Resnet50 = 100*np.sum(np.array(Resnet50_predictions)==np.argmax(test_targets, axis=1))/len(Resnet50_predictions)
print('Resnet50 Test accuracy: %.4f%%' % test_accuracy_Resnet50)
-- VGG19_predictions Loaded --- -- Xception_predictions Loaded --- -- InceptionV3_predictions Loaded --- -- Resnet50_predictions Loaded --- VGG19 Test accuracy: 49.7608% Xception Test accuracy: 85.0478% Inception Test accuracy: 77.1531% Resnet50 Test accuracy: 81.2201%
Xception , Inception and Resnet provides Test accuracy higher than 60%
Xception Test accuracy: 85.0478%
Inception Test accuracy: 77.1531%
Resnet50 Test accuracy: 81.2201%
Write a function that takes an image path as input and returns the dog breed (Affenpinscher, Afghan_hound, etc) that is predicted by your model.
Similar to the analogous function in Step 5, your function should have three steps:
dog_names array defined in Step 0 of this notebook to return the corresponding breed.The functions to extract the bottleneck features can be found in extract_bottleneck_features.py, and they have been imported in an earlier code cell. To obtain the bottleneck features corresponding to your chosen CNN architecture, you need to use the function
extract_{network}
where {network}, in the above filename, should be one of VGG19, Resnet50, InceptionV3, or Xception.
### TODO: Write a function that takes a path to an image as input
### and returns the dog breed that is predicted by the model.
def predict_breed_for_dog(img_path):
# extract bottleneck features
bottleneck_feature = extract_Xception(path_to_tensor(img_path))
# obtain predicted vector
predicted_vector = Xception_model.predict(bottleneck_feature)
# return dog breed that is predicted by the model
return dog_names[np.argmax(predicted_vector)]
print(' I am done with predict_breed_for_dog ')
I am done with predict_breed_for_dog
Write an algorithm that accepts a file path to an image and first determines whether the image contains a human, dog, or neither. Then,
You are welcome to write your own functions for detecting humans and dogs in images, but feel free to use the face_detector and dog_detector functions developed above. You are required to use your CNN from Step 5 to predict dog breed.
Some sample output for our algorithm is provided below, but feel free to design your own user experience!

### TODO: Write your algorithm.
### Feel free to use as many code cells as needed.
def dog_human_dog_NA_detector(img_path):
img = cv2.imread(img_path)
cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
return cv_rgb
fig = plt.figure(figsize=(20, 8))
for i in range(0,8):
img_path_n=train_files[i]
dog_breed = predict_breed_for_dog(img_path_n)
ax = fig.add_subplot(2, 4, i + 1, xticks=[], yticks=[])
ax.imshow(dog_human_dog_NA_detector(img_path_n))
ax.set_title("{} {} ({})".format(i,("Dog Breed :" if dog_detector(img_path_n) else "Human Looks Like :" if face_detector(img_path_n)
else "Error "),dog_breed),color=("green" if dog_detector(img_path_n)
else "red" if face_detector(img_path_n)
else "black"))
def dog_breed_image_read(img_path):
breed = predict_breed_for_dog(img_path)
# Display the image
img = cv2.imread(img_path)
cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# plt.imshow(cv_rgb)
# plt.show()
return cv_rgb
#print(dog_breed_image_read(test_files))
#print("\n",dog_names)
#predict_breed_for_dog(test_files)
fig = plt.figure(figsize=(20, 8))
for i in range(0,8):
#print(dog_names [np.argmax(test_targets[i])]) #true_idx
#print((test_files[i]))
#print(predict_breed_for_dog(test_files[i])) #pred_idx
ax = fig.add_subplot(2, 4, i + 1, xticks=[], yticks=[])
ax.imshow(dog_breed_image_read(test_files[i]))
pred_idx = predict_breed_for_dog(test_files[i])
true_idx = dog_names [np.argmax(test_targets[i])]
#print(pred_idx)
#print(true_idx)
ax.set_title("{} {} ({})".format(i,pred_idx, true_idx),color=("green" if pred_idx == true_idx else "red"))
In this section, you will take your new algorithm for a spin! What kind of dog does the algorithm think that you look like? If you have a dog, does it predict your dog's breed accurately? If you have a cat, does it mistakenly think that your cat is a dog?
Test your algorithm at least six images on your computer. Feel free to use any images you like. Use at least two human and two dog images.
Question 6: Is the output better than you expected :) ? Or worse :( ? Provide at least three possible points of improvement for your algorithm.
Answer: The below results on my 7 sample images is as per my expectations and have correctly identified however if you see the above training results there is one error in the test data sets where it has identified wrongly Belgian Sheep dogs as Ginat Schnazuer. These are very similar breeds and my program could not clearly identify the same.
Items I would consider to improve algorithim will be (mainly to handle dual images next to each other, angular shift -- dog lying down horizontally and only portion of face visible, image pixcel distortation etc)
## TODO: Execute your algorithm from Step 6 on
## at least 6 images on your computer.
## Feel free to use as many code cells as needed.
image_sample_files = np.array(glob("samples/*"))
print(image_sample_files)
['samples\\AllenGreenspan.jpg' 'samples\\Brittany_02625.jpg' 'samples\\Curly-coated_retriever_03896.jpg' 'samples\\Diana.jpg' 'samples\\Hullo0.JPG' 'samples\\Labrador_retriever_06449.jpg' 'samples\\lion.jpeg']
fig = plt.figure(figsize=(20, 8))
for i,image_sample_file in enumerate(image_sample_files):
img_path_n=image_sample_file
dog_breed = predict_breed_for_dog(img_path_n)
ax = fig.add_subplot(2, 4, i + 1, xticks=[], yticks=[])
ax.imshow(dog_human_dog_NA_detector(img_path_n))
ax.set_title("{} {} ({})".format(i,("Dog Breed :" if dog_detector(img_path_n) else "Human Looks Like :" if face_detector(img_path_n)
else "Neither but close match to dog breed:"),dog_breed),color=("green" if dog_detector(img_path_n)
else "red" if face_detector(img_path_n)
else "black"))






